├── .ipynb_checkpoints ├── Loading the MNIST Dataset-checkpoint.ipynb ├── README-checkpoint.md └── playing with numpy library-checkpoint.ipynb ├── Data └── MNIST │ └── raw │ └── train-images-idx3-ubyte.gz ├── Exercise Notebooks ├── .ipynb_checkpoints │ ├── How_to_Define_Functional_And_Sequential_Keras_Models-checkpoint.ipynb │ ├── Loading the MNIST Dataset-checkpoint.ipynb │ ├── NumPy_Exercises_From_Udemy_Course-checkpoint.ipynb │ ├── Pandas_Exercises_From_Udemy_Course-checkpoint.ipynb │ ├── Playing with numpy library-checkpoint.ipynb │ ├── Playing_With_Matplotlib_Library-checkpoint.ipynb │ ├── Playing_With_Numpy_Library-checkpoint.ipynb │ ├── Playing_With_Pandas_Library-checkpoint.ipynb │ ├── Playing_With_Pytorch_Library-checkpoint.ipynb │ ├── Playing_With_Scipy_Library-checkpoint.ipynb │ ├── Playing_With_Seaborn_Library-checkpoint.ipynb │ ├── Training_a_Convolutional_Dense_Layer_With_Recognition_Model_Using_Keras_on_the_CIFAR_10_Dataset-checkpoint.ipynb │ ├── Training_a_Convolutional_Layer_With_Recognition_Model_Using_Pytorch_on_the_MNIST_Dataset-checkpoint.ipynb │ ├── Training_a_Digit_Recognition_Model_Using_Keras_on_the_MNIST_Dataset-checkpoint.ipynb │ ├── Training_a_Digit_Recognition_Model_Using_Pytorch_Library_on_the_MNIST_Dataset-checkpoint.ipynb │ ├── Training_a_Learning_Model_Using_Keras_for_Digit_Recognition_on_the_MNIST_Dataset-checkpoint.ipynb │ ├── Training_a_Recognition_Model_Using_Keras_on_the_CIFAR_10_Dataset-checkpoint.ipynb │ └── Training_a_Recognition_Model_Using_Keras_on_the_CIFAR_10_Dataset.ipynb-checkpoint.ipynb ├── Data │ ├── Data_model_CIFAR-10.h5 │ ├── Data_model_CIFAR-10.json │ ├── Hand_Writing_Number_4.png │ ├── Universities.csv │ ├── Yasin Rezvani.jpg │ ├── Yasin Rezvani_grayscale.jpg │ ├── Yasin Rezvani_resized.jpg │ ├── african_econ_crises.csv │ ├── face_from_scipy_misc_library.png │ ├── heart.csv │ ├── iris.csv │ ├── model.h5 │ ├── model.json │ ├── outputfromDataFrame.csv │ └── outputfromDataFramewithindex.csv ├── How_to_Define_Functional_And_Sequential_Keras_Models.ipynb ├── Loading_The_MNIST_Dataset.ipynb ├── NumPy_Exercises_From_Udemy_Course.ipynb ├── Pandas_Exercises_From_Udemy_Course.ipynb ├── Playing_With_Matplotlib_Library.ipynb ├── Playing_With_Numpy_Library.ipynb ├── Playing_With_Pandas_Library.ipynb ├── Playing_With_Scipy_Library.ipynb ├── Playing_With_Seaborn_Library.ipynb ├── Training_a_Convolutional_Dense_Layer_With_Recognition_Model_Using_Keras_on_the_CIFAR_10_Dataset.ipynb ├── Training_a_Convolutional_Layer_With_Recognition_Model_Using_Pytorch_on_the_MNIST_Dataset.ipynb ├── Training_a_Digit_Recognition_Model_Using_Keras_on_the_MNIST_Dataset.ipynb ├── Training_a_Digit_Recognition_Model_Using_Pytorch_Library_on_the_MNIST_Dataset.ipynb └── Training_a_Recognition_Model_Using_Keras_on_the_CIFAR_10_Dataset.ipynb ├── LICENSE.txt └── README.md /.ipynb_checkpoints/Loading the MNIST Dataset-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "id": "c41bbec6-578e-406d-b67f-aaebbba89e15", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "ename": "ModuleNotFoundError", 11 | "evalue": "No module named 'tensorflow'", 12 | "output_type": "error", 13 | "traceback": [ 14 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 15 | "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", 16 | "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mnist\n", 17 | "File \u001b[1;32mc:\\users\\yasin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\keras\\__init__.py:3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"AUTOGENERATED. DO NOT EDIT.\"\"\"\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __internal__\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n", 18 | "File \u001b[1;32mc:\\users\\yasin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\keras\\__internal__\\__init__.py:3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"AUTOGENERATED. DO NOT EDIT.\"\"\"\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m__internal__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m__internal__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m layers\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m__internal__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m losses\n", 19 | "File \u001b[1;32mc:\\users\\yasin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\keras\\__internal__\\backend\\__init__.py:3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"AUTOGENERATED. DO NOT EDIT.\"\"\"\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _initialize_variables \u001b[38;5;28;01mas\u001b[39;00m initialize_variables\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m track_variable\n", 20 | "File \u001b[1;32mc:\\users\\yasin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\keras\\src\\__init__.py:21\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;66;03m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;124;03m\"\"\"Implementation of the Keras API, the high-level API of TensorFlow.\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \n\u001b[0;32m 17\u001b[0m \u001b[38;5;124;03mDetailed documentation and user guides are available at\u001b[39;00m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;124;03m[keras.io](https://keras.io).\u001b[39;00m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[0;32m 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m distribute\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m models\n", 21 | "File \u001b[1;32mc:\\users\\yasin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\keras\\src\\applications\\__init__.py:18\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;66;03m# ==============================================================================\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;124;03m\"\"\"Keras Applications are premade architectures with pre-trained weights.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapplications\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvnext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConvNeXtBase\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapplications\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvnext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConvNeXtLarge\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapplications\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvnext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConvNeXtSmall\n", 22 | "File \u001b[1;32mc:\\users\\yasin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\keras\\src\\applications\\convnext.py:26\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;124;03m\"\"\"ConvNeXt models for Keras.\u001b[39;00m\n\u001b[0;32m 18\u001b[0m \n\u001b[0;32m 19\u001b[0m \u001b[38;5;124;03mReferences:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[38;5;124;03m (CVPR 2022)\u001b[39;00m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 25\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m---> 26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[0;32m 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m initializers\n", 23 | "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'" 24 | ] 25 | } 26 | ], 27 | "source": [ 28 | "import numpy as np\n", 29 | "from keras.datasets import mnist" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "id": "f93cfc01-aa90-48ae-9883-6423a3216138", 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [] 39 | } 40 | ], 41 | "metadata": { 42 | "kernelspec": { 43 | "display_name": "Python 3 (ipykernel)", 44 | "language": "python", 45 | "name": "python3" 46 | }, 47 | "language_info": { 48 | "codemirror_mode": { 49 | "name": "ipython", 50 | "version": 3 51 | }, 52 | "file_extension": ".py", 53 | "mimetype": "text/x-python", 54 | "name": "python", 55 | "nbconvert_exporter": "python", 56 | "pygments_lexer": "ipython3", 57 | "version": "3.8.10" 58 | } 59 | }, 60 | "nbformat": 4, 61 | "nbformat_minor": 5 62 | } 63 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/README-checkpoint.md: -------------------------------------------------------------------------------- 1 | # Python_Learning_Practice_Notebook 2 | 3 | ## I plan to kick off soon and will share my progress with you, take care...:) 4 | 5 | #### i'm gonna make it 6 | 7 | check it connceted to msi laptop 8 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/playing with numpy library-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "b9499a42-604e-41af-889d-140cec4baace", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 10, 16 | "id": "38d272a3-447f-4636-8341-76827f10a53a", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "first_array = np.array([[10,20,30],[40,50,60]])" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 21, 26 | "id": "454e89e1-c805-4845-9e84-0ae931dfd351", 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "[[10 20 30]\n", 34 | " [40 50 60]]\n" 35 | ] 36 | } 37 | ], 38 | "source": [ 39 | "print(first_array)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 19, 45 | "id": "087623e2-973f-4208-9335-5557b11d42c6", 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "name": "stdout", 50 | "output_type": "stream", 51 | "text": [ 52 | "[10 20 30]\n", 53 | "[40 50 60]\n" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "for i in first_array:\n", 59 | " print(i)" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 20, 65 | "id": "b6f37ae7-e18a-46ca-8945-16beed12a5cf", 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "data": { 70 | "text/plain": [ 71 | "numpy.ndarray" 72 | ] 73 | }, 74 | "execution_count": 20, 75 | "metadata": {}, 76 | "output_type": "execute_result" 77 | } 78 | ], 79 | "source": [ 80 | "type(first_array)" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 27, 86 | "id": "8df5ae2f-4686-4ea3-a1c6-5399d4baa8f7", 87 | "metadata": {}, 88 | "outputs": [], 89 | "source": [ 90 | "second_array = np.array(range(100))" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 33, 96 | "id": "2fe72892-2c3c-4511-ac1c-3c448c5c06c5", 97 | "metadata": {}, 98 | "outputs": [ 99 | { 100 | "name": "stdout", 101 | "output_type": "stream", 102 | "text": [ 103 | "[[ 0 1 2 3 4 5 6 7 8 9]\n", 104 | " [10 11 12 13 14 15 16 17 18 19]\n", 105 | " [20 21 22 23 24 25 26 27 28 29]\n", 106 | " [30 31 32 33 34 35 36 37 38 39]\n", 107 | " [40 41 42 43 44 45 46 47 48 49]\n", 108 | " [50 51 52 53 54 55 56 57 58 59]\n", 109 | " [60 61 62 63 64 65 66 67 68 69]\n", 110 | " [70 71 72 73 74 75 76 77 78 79]\n", 111 | " [80 81 82 83 84 85 86 87 88 89]\n", 112 | " [90 91 92 93 94 95 96 97 98 99]]\n" 113 | ] 114 | } 115 | ], 116 | "source": [ 117 | "print(second_array.reshape(10,10))" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": null, 123 | "id": "063b49a8-c352-4551-8a27-27287ca1d8df", 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [] 127 | } 128 | ], 129 | "metadata": { 130 | "kernelspec": { 131 | "display_name": "Python 3 (ipykernel)", 132 | "language": "python", 133 | "name": "python3" 134 | }, 135 | "language_info": { 136 | "codemirror_mode": { 137 | "name": "ipython", 138 | "version": 3 139 | }, 140 | "file_extension": ".py", 141 | "mimetype": "text/x-python", 142 | "name": "python", 143 | "nbconvert_exporter": "python", 144 | "pygments_lexer": "ipython3", 145 | "version": "3.8.10" 146 | } 147 | }, 148 | "nbformat": 4, 149 | "nbformat_minor": 5 150 | } 151 | -------------------------------------------------------------------------------- /Data/MNIST/raw/train-images-idx3-ubyte.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YasinRezvani/Hands_On_Machine_Learning_and_Deep_Learning_with_Python/0f1876aa3e18636857a38aac46fc2f0843a009f8/Data/MNIST/raw/train-images-idx3-ubyte.gz -------------------------------------------------------------------------------- /Exercise Notebooks/.ipynb_checkpoints/NumPy_Exercises_From_Udemy_Course-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "___\n", 8 | "\n", 9 | "\n", 10 | "___\n", 11 | "
Copyright Pierian Data
\n", 12 | "
For more information, visit us at www.pieriandata.com
" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "# NumPy Exercises\n", 20 | "\n", 21 | "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks and then you'll be asked some more complicated questions.\n", 22 | "\n", 23 | "
IMPORTANT NOTE! Make sure you don't run the cells directly above the example output shown,
otherwise you will end up writing over the example output!
" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "#### 1. Import NumPy as np" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 2, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "import numpy as np" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "#### 2. Create an array of 10 zeros " 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 2, 52 | "metadata": {}, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" 58 | ] 59 | }, 60 | "execution_count": 2, 61 | "metadata": {}, 62 | "output_type": "execute_result" 63 | } 64 | ], 65 | "source": [ 66 | "# CODE HERE\n", 67 | "np.zeros(10)" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 2, 73 | "metadata": {}, 74 | "outputs": [ 75 | { 76 | "data": { 77 | "text/plain": [ 78 | "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" 79 | ] 80 | }, 81 | "execution_count": 2, 82 | "metadata": {}, 83 | "output_type": "execute_result" 84 | } 85 | ], 86 | "source": [ 87 | "# DON'T WRITE HERE" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "#### 3. Create an array of 10 ones" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 3, 100 | "metadata": {}, 101 | "outputs": [ 102 | { 103 | "data": { 104 | "text/plain": [ 105 | "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" 106 | ] 107 | }, 108 | "execution_count": 3, 109 | "metadata": {}, 110 | "output_type": "execute_result" 111 | } 112 | ], 113 | "source": [ 114 | "np.ones(10)" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 3, 120 | "metadata": {}, 121 | "outputs": [ 122 | { 123 | "data": { 124 | "text/plain": [ 125 | "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" 126 | ] 127 | }, 128 | "execution_count": 3, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "# DON'T WRITE HERE" 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "metadata": {}, 140 | "source": [ 141 | "#### 4. Create an array of 10 fives" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 7, 147 | "metadata": {}, 148 | "outputs": [ 149 | { 150 | "data": { 151 | "text/plain": [ 152 | "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" 153 | ] 154 | }, 155 | "execution_count": 7, 156 | "metadata": {}, 157 | "output_type": "execute_result" 158 | } 159 | ], 160 | "source": [ 161 | "np.zeros(10) + 5" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": 4, 167 | "metadata": {}, 168 | "outputs": [ 169 | { 170 | "data": { 171 | "text/plain": [ 172 | "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" 173 | ] 174 | }, 175 | "execution_count": 4, 176 | "metadata": {}, 177 | "output_type": "execute_result" 178 | } 179 | ], 180 | "source": [ 181 | "# DON'T WRITE HERE" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "#### 5. Create an array of the integers from 10 to 50" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 8, 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "data": { 198 | "text/plain": [ 199 | "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", 200 | " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n", 201 | " 44, 45, 46, 47, 48, 49, 50])" 202 | ] 203 | }, 204 | "execution_count": 8, 205 | "metadata": {}, 206 | "output_type": "execute_result" 207 | } 208 | ], 209 | "source": [ 210 | "np.arange(10,51)" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 5, 216 | "metadata": {}, 217 | "outputs": [ 218 | { 219 | "data": { 220 | "text/plain": [ 221 | "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", 222 | " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n", 223 | " 44, 45, 46, 47, 48, 49, 50])" 224 | ] 225 | }, 226 | "execution_count": 5, 227 | "metadata": {}, 228 | "output_type": "execute_result" 229 | } 230 | ], 231 | "source": [ 232 | "# DON'T WRITE HERE" 233 | ] 234 | }, 235 | { 236 | "cell_type": "markdown", 237 | "metadata": {}, 238 | "source": [ 239 | "#### 6. Create an array of all the even integers from 10 to 50" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": 9, 245 | "metadata": {}, 246 | "outputs": [ 247 | { 248 | "data": { 249 | "text/plain": [ 250 | "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n", 251 | " 44, 46, 48, 50])" 252 | ] 253 | }, 254 | "execution_count": 9, 255 | "metadata": {}, 256 | "output_type": "execute_result" 257 | } 258 | ], 259 | "source": [ 260 | "np.arange(10,51,2)" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 6, 266 | "metadata": {}, 267 | "outputs": [ 268 | { 269 | "data": { 270 | "text/plain": [ 271 | "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n", 272 | " 44, 46, 48, 50])" 273 | ] 274 | }, 275 | "execution_count": 6, 276 | "metadata": {}, 277 | "output_type": "execute_result" 278 | } 279 | ], 280 | "source": [ 281 | "# DON'T WRITE HERE" 282 | ] 283 | }, 284 | { 285 | "cell_type": "markdown", 286 | "metadata": {}, 287 | "source": [ 288 | "#### 7. Create a 3x3 matrix with values ranging from 0 to 8" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 11, 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "array([[0, 1, 2],\n", 300 | " [3, 4, 5],\n", 301 | " [6, 7, 8]])" 302 | ] 303 | }, 304 | "execution_count": 11, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | } 308 | ], 309 | "source": [ 310 | "np.arange(0,9).reshape(3,3)" 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": 7, 316 | "metadata": {}, 317 | "outputs": [ 318 | { 319 | "data": { 320 | "text/plain": [ 321 | "array([[0, 1, 2],\n", 322 | " [3, 4, 5],\n", 323 | " [6, 7, 8]])" 324 | ] 325 | }, 326 | "execution_count": 7, 327 | "metadata": {}, 328 | "output_type": "execute_result" 329 | } 330 | ], 331 | "source": [ 332 | "# DON'T WRITE HERE" 333 | ] 334 | }, 335 | { 336 | "cell_type": "markdown", 337 | "metadata": {}, 338 | "source": [ 339 | "#### 8. Create a 3x3 identity matrix" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": 12, 345 | "metadata": {}, 346 | "outputs": [ 347 | { 348 | "data": { 349 | "text/plain": [ 350 | "array([[1., 0., 0.],\n", 351 | " [0., 1., 0.],\n", 352 | " [0., 0., 1.]])" 353 | ] 354 | }, 355 | "execution_count": 12, 356 | "metadata": {}, 357 | "output_type": "execute_result" 358 | } 359 | ], 360 | "source": [ 361 | "np.eye(3,3)" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 8, 367 | "metadata": {}, 368 | "outputs": [ 369 | { 370 | "data": { 371 | "text/plain": [ 372 | "array([[1., 0., 0.],\n", 373 | " [0., 1., 0.],\n", 374 | " [0., 0., 1.]])" 375 | ] 376 | }, 377 | "execution_count": 8, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "# DON'T WRITE HERE" 384 | ] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": {}, 389 | "source": [ 390 | "#### 9. Use NumPy to generate a random number between 0 and 1

 NOTE: Your result's value should be different from the one shown below." 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 30, 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "data": { 400 | "text/plain": [ 401 | "array([0.37454012])" 402 | ] 403 | }, 404 | "execution_count": 30, 405 | "metadata": {}, 406 | "output_type": "execute_result" 407 | } 408 | ], 409 | "source": [ 410 | "np.random.seed(42)\n", 411 | "np.random.rand(1)" 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "execution_count": 28, 417 | "metadata": {}, 418 | "outputs": [], 419 | "source": [ 420 | "# DON'T WRITE HERE" 421 | ] 422 | }, 423 | { 424 | "cell_type": "markdown", 425 | "metadata": {}, 426 | "source": [ 427 | "#### 10. Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution

  NOTE: Your result's values should be different from the ones shown below." 428 | ] 429 | }, 430 | { 431 | "cell_type": "code", 432 | "execution_count": 33, 433 | "metadata": {}, 434 | "outputs": [ 435 | { 436 | "data": { 437 | "text/plain": [ 438 | "array([ 0.49671415, -0.1382643 , 0.64768854, 1.52302986, -0.23415337,\n", 439 | " -0.23413696, 1.57921282, 0.76743473, -0.46947439, 0.54256004,\n", 440 | " -0.46341769, -0.46572975, 0.24196227, -1.91328024, -1.72491783,\n", 441 | " -0.56228753, -1.01283112, 0.31424733, -0.90802408, -1.4123037 ,\n", 442 | " 1.46564877, -0.2257763 , 0.0675282 , -1.42474819, -0.54438272])" 443 | ] 444 | }, 445 | "execution_count": 33, 446 | "metadata": {}, 447 | "output_type": "execute_result" 448 | } 449 | ], 450 | "source": [ 451 | "np.random.seed(42)\n", 452 | "np.random.randn(25)" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 10, 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "data": { 462 | "text/plain": [ 463 | "array([ 1.80076712, -1.12375847, -0.98524305, 0.11673573, 1.96346762,\n", 464 | " 1.81378592, -0.33790771, 0.85012656, 0.0100703 , -0.91005957,\n", 465 | " 0.29064366, 0.69906357, 0.1774377 , -0.61958694, -0.45498611,\n", 466 | " -2.0804685 , -0.06778549, 1.06403819, 0.4311884 , -1.09853837,\n", 467 | " 1.11980469, -0.48751963, 1.32517611, -0.61775122, -0.00622865])" 468 | ] 469 | }, 470 | "execution_count": 10, 471 | "metadata": {}, 472 | "output_type": "execute_result" 473 | } 474 | ], 475 | "source": [ 476 | "# DON'T WRITE HERE" 477 | ] 478 | }, 479 | { 480 | "cell_type": "markdown", 481 | "metadata": {}, 482 | "source": [ 483 | "#### 11. Create the following matrix:" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": 5, 489 | "metadata": {}, 490 | "outputs": [ 491 | { 492 | "data": { 493 | "text/plain": [ 494 | "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", 495 | " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", 496 | " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", 497 | " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", 498 | " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", 499 | " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", 500 | " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", 501 | " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", 502 | " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", 503 | " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" 504 | ] 505 | }, 506 | "execution_count": 5, 507 | "metadata": {}, 508 | "output_type": "execute_result" 509 | } 510 | ], 511 | "source": [ 512 | "np.arange(0.01,1.01,0.01,dtype=float).reshape(10,10)\n", 513 | "np.arange(1,101).reshape(10,10) / 100" 514 | ] 515 | }, 516 | { 517 | "cell_type": "code", 518 | "execution_count": 11, 519 | "metadata": {}, 520 | "outputs": [ 521 | { 522 | "data": { 523 | "text/plain": [ 524 | "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", 525 | " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", 526 | " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", 527 | " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", 528 | " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", 529 | " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", 530 | " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", 531 | " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", 532 | " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", 533 | " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" 534 | ] 535 | }, 536 | "execution_count": 11, 537 | "metadata": {}, 538 | "output_type": "execute_result" 539 | } 540 | ], 541 | "source": [ 542 | "# DON'T WRITE HERE" 543 | ] 544 | }, 545 | { 546 | "cell_type": "markdown", 547 | "metadata": {}, 548 | "source": [ 549 | "#### 12. Create an array of 20 linearly spaced points between 0 and 1:" 550 | ] 551 | }, 552 | { 553 | "cell_type": "code", 554 | "execution_count": 46, 555 | "metadata": {}, 556 | "outputs": [ 557 | { 558 | "data": { 559 | "text/plain": [ 560 | "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", 561 | " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", 562 | " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", 563 | " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" 564 | ] 565 | }, 566 | "execution_count": 46, 567 | "metadata": {}, 568 | "output_type": "execute_result" 569 | } 570 | ], 571 | "source": [ 572 | "np.linspace(0,1,20)" 573 | ] 574 | }, 575 | { 576 | "cell_type": "code", 577 | "execution_count": 12, 578 | "metadata": {}, 579 | "outputs": [ 580 | { 581 | "data": { 582 | "text/plain": [ 583 | "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", 584 | " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", 585 | " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", 586 | " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" 587 | ] 588 | }, 589 | "execution_count": 12, 590 | "metadata": {}, 591 | "output_type": "execute_result" 592 | } 593 | ], 594 | "source": [ 595 | "# DON'T WRITE HERE" 596 | ] 597 | }, 598 | { 599 | "cell_type": "markdown", 600 | "metadata": {}, 601 | "source": [ 602 | "## Numpy Indexing and Selection\n", 603 | "\n", 604 | "Now you will be given a starting matrix (be sure to run the cell below!), and be asked to replicate the resulting matrix outputs:" 605 | ] 606 | }, 607 | { 608 | "cell_type": "code", 609 | "execution_count": 48, 610 | "metadata": {}, 611 | "outputs": [ 612 | { 613 | "data": { 614 | "text/plain": [ 615 | "array([[ 1, 2, 3, 4, 5],\n", 616 | " [ 6, 7, 8, 9, 10],\n", 617 | " [11, 12, 13, 14, 15],\n", 618 | " [16, 17, 18, 19, 20],\n", 619 | " [21, 22, 23, 24, 25]])" 620 | ] 621 | }, 622 | "execution_count": 48, 623 | "metadata": {}, 624 | "output_type": "execute_result" 625 | } 626 | ], 627 | "source": [ 628 | "# RUN THIS CELL - THIS IS OUR STARTING MATRIX\n", 629 | "mat = np.arange(1,26).reshape(5,5)\n", 630 | "mat" 631 | ] 632 | }, 633 | { 634 | "cell_type": "markdown", 635 | "metadata": {}, 636 | "source": [ 637 | "#### 13. Write code that reproduces the output shown below.

  Be careful not to run the cell immediately above the output, otherwise you won't be able to see the output any more." 638 | ] 639 | }, 640 | { 641 | "cell_type": "code", 642 | "execution_count": 56, 643 | "metadata": {}, 644 | "outputs": [ 645 | { 646 | "data": { 647 | "text/plain": [ 648 | "array([[12, 13, 14, 15],\n", 649 | " [17, 18, 19, 20],\n", 650 | " [22, 23, 24, 25]])" 651 | ] 652 | }, 653 | "execution_count": 56, 654 | "metadata": {}, 655 | "output_type": "execute_result" 656 | } 657 | ], 658 | "source": [ 659 | "# CODE HERE\n", 660 | "mat[2:,1:]" 661 | ] 662 | }, 663 | { 664 | "cell_type": "code", 665 | "execution_count": 14, 666 | "metadata": {}, 667 | "outputs": [ 668 | { 669 | "data": { 670 | "text/plain": [ 671 | "array([[12, 13, 14, 15],\n", 672 | " [17, 18, 19, 20],\n", 673 | " [22, 23, 24, 25]])" 674 | ] 675 | }, 676 | "execution_count": 14, 677 | "metadata": {}, 678 | "output_type": "execute_result" 679 | } 680 | ], 681 | "source": [ 682 | "# DON'T WRITE HERE" 683 | ] 684 | }, 685 | { 686 | "cell_type": "markdown", 687 | "metadata": {}, 688 | "source": [ 689 | "#### 14. Write code that reproduces the output shown below." 690 | ] 691 | }, 692 | { 693 | "cell_type": "code", 694 | "execution_count": 57, 695 | "metadata": {}, 696 | "outputs": [ 697 | { 698 | "data": { 699 | "text/plain": [ 700 | "20" 701 | ] 702 | }, 703 | "execution_count": 57, 704 | "metadata": {}, 705 | "output_type": "execute_result" 706 | } 707 | ], 708 | "source": [ 709 | "mat[3,4]" 710 | ] 711 | }, 712 | { 713 | "cell_type": "code", 714 | "execution_count": 15, 715 | "metadata": {}, 716 | "outputs": [ 717 | { 718 | "data": { 719 | "text/plain": [ 720 | "20" 721 | ] 722 | }, 723 | "execution_count": 15, 724 | "metadata": {}, 725 | "output_type": "execute_result" 726 | } 727 | ], 728 | "source": [ 729 | "# DON'T WRITE HERE" 730 | ] 731 | }, 732 | { 733 | "cell_type": "markdown", 734 | "metadata": {}, 735 | "source": [ 736 | "#### 15. Write code that reproduces the output shown below." 737 | ] 738 | }, 739 | { 740 | "cell_type": "code", 741 | "execution_count": 58, 742 | "metadata": {}, 743 | "outputs": [ 744 | { 745 | "data": { 746 | "text/plain": [ 747 | "array([[ 2],\n", 748 | " [ 7],\n", 749 | " [12]])" 750 | ] 751 | }, 752 | "execution_count": 58, 753 | "metadata": {}, 754 | "output_type": "execute_result" 755 | } 756 | ], 757 | "source": [ 758 | "mat[:3,1:2]" 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": 16, 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "array([[ 2],\n", 770 | " [ 7],\n", 771 | " [12]])" 772 | ] 773 | }, 774 | "execution_count": 16, 775 | "metadata": {}, 776 | "output_type": "execute_result" 777 | } 778 | ], 779 | "source": [ 780 | "# DON'T WRITE HERE" 781 | ] 782 | }, 783 | { 784 | "cell_type": "markdown", 785 | "metadata": {}, 786 | "source": [ 787 | "#### 16. Write code that reproduces the output shown below." 788 | ] 789 | }, 790 | { 791 | "cell_type": "code", 792 | "execution_count": 61, 793 | "metadata": {}, 794 | "outputs": [ 795 | { 796 | "data": { 797 | "text/plain": [ 798 | "array([21, 22, 23, 24, 25])" 799 | ] 800 | }, 801 | "execution_count": 61, 802 | "metadata": {}, 803 | "output_type": "execute_result" 804 | } 805 | ], 806 | "source": [ 807 | "mat[4]" 808 | ] 809 | }, 810 | { 811 | "cell_type": "code", 812 | "execution_count": 17, 813 | "metadata": {}, 814 | "outputs": [ 815 | { 816 | "data": { 817 | "text/plain": [ 818 | "array([21, 22, 23, 24, 25])" 819 | ] 820 | }, 821 | "execution_count": 17, 822 | "metadata": {}, 823 | "output_type": "execute_result" 824 | } 825 | ], 826 | "source": [ 827 | "# DON'T WRITE HERE" 828 | ] 829 | }, 830 | { 831 | "cell_type": "markdown", 832 | "metadata": {}, 833 | "source": [ 834 | "#### 17. Write code that reproduces the output shown below." 835 | ] 836 | }, 837 | { 838 | "cell_type": "code", 839 | "execution_count": 62, 840 | "metadata": {}, 841 | "outputs": [ 842 | { 843 | "data": { 844 | "text/plain": [ 845 | "array([[16, 17, 18, 19, 20],\n", 846 | " [21, 22, 23, 24, 25]])" 847 | ] 848 | }, 849 | "execution_count": 62, 850 | "metadata": {}, 851 | "output_type": "execute_result" 852 | } 853 | ], 854 | "source": [ 855 | "mat[3:,:]" 856 | ] 857 | }, 858 | { 859 | "cell_type": "code", 860 | "execution_count": 18, 861 | "metadata": {}, 862 | "outputs": [ 863 | { 864 | "data": { 865 | "text/plain": [ 866 | "array([[16, 17, 18, 19, 20],\n", 867 | " [21, 22, 23, 24, 25]])" 868 | ] 869 | }, 870 | "execution_count": 18, 871 | "metadata": {}, 872 | "output_type": "execute_result" 873 | } 874 | ], 875 | "source": [ 876 | "# DON'T WRITE HERE" 877 | ] 878 | }, 879 | { 880 | "cell_type": "markdown", 881 | "metadata": {}, 882 | "source": [ 883 | "## NumPy Operations" 884 | ] 885 | }, 886 | { 887 | "cell_type": "markdown", 888 | "metadata": {}, 889 | "source": [ 890 | "#### 18. Get the sum of all the values in mat" 891 | ] 892 | }, 893 | { 894 | "cell_type": "code", 895 | "execution_count": 63, 896 | "metadata": {}, 897 | "outputs": [ 898 | { 899 | "data": { 900 | "text/plain": [ 901 | "325" 902 | ] 903 | }, 904 | "execution_count": 63, 905 | "metadata": {}, 906 | "output_type": "execute_result" 907 | } 908 | ], 909 | "source": [ 910 | "mat.sum()" 911 | ] 912 | }, 913 | { 914 | "cell_type": "code", 915 | "execution_count": 19, 916 | "metadata": {}, 917 | "outputs": [ 918 | { 919 | "data": { 920 | "text/plain": [ 921 | "325" 922 | ] 923 | }, 924 | "execution_count": 19, 925 | "metadata": {}, 926 | "output_type": "execute_result" 927 | } 928 | ], 929 | "source": [ 930 | "# DON'T WRITE HERE" 931 | ] 932 | }, 933 | { 934 | "cell_type": "markdown", 935 | "metadata": {}, 936 | "source": [ 937 | "#### 19. Get the standard deviation of the values in mat" 938 | ] 939 | }, 940 | { 941 | "cell_type": "code", 942 | "execution_count": 65, 943 | "metadata": {}, 944 | "outputs": [ 945 | { 946 | "data": { 947 | "text/plain": [ 948 | "7.211102550927978" 949 | ] 950 | }, 951 | "execution_count": 65, 952 | "metadata": {}, 953 | "output_type": "execute_result" 954 | } 955 | ], 956 | "source": [ 957 | "mat.std()" 958 | ] 959 | }, 960 | { 961 | "cell_type": "code", 962 | "execution_count": 20, 963 | "metadata": {}, 964 | "outputs": [ 965 | { 966 | "data": { 967 | "text/plain": [ 968 | "7.211102550927978" 969 | ] 970 | }, 971 | "execution_count": 20, 972 | "metadata": {}, 973 | "output_type": "execute_result" 974 | } 975 | ], 976 | "source": [ 977 | "# DON'T WRITE HERE" 978 | ] 979 | }, 980 | { 981 | "cell_type": "markdown", 982 | "metadata": {}, 983 | "source": [ 984 | "#### 20. Get the sum of all the columns in mat" 985 | ] 986 | }, 987 | { 988 | "cell_type": "code", 989 | "execution_count": 66, 990 | "metadata": {}, 991 | "outputs": [ 992 | { 993 | "data": { 994 | "text/plain": [ 995 | "array([55, 60, 65, 70, 75])" 996 | ] 997 | }, 998 | "execution_count": 66, 999 | "metadata": {}, 1000 | "output_type": "execute_result" 1001 | } 1002 | ], 1003 | "source": [ 1004 | "mat.sum(axis=0)" 1005 | ] 1006 | }, 1007 | { 1008 | "cell_type": "code", 1009 | "execution_count": 21, 1010 | "metadata": {}, 1011 | "outputs": [ 1012 | { 1013 | "data": { 1014 | "text/plain": [ 1015 | "array([55, 60, 65, 70, 75])" 1016 | ] 1017 | }, 1018 | "execution_count": 21, 1019 | "metadata": {}, 1020 | "output_type": "execute_result" 1021 | } 1022 | ], 1023 | "source": [ 1024 | "# DON'T WRITE HERE" 1025 | ] 1026 | }, 1027 | { 1028 | "cell_type": "markdown", 1029 | "metadata": {}, 1030 | "source": [ 1031 | "## Bonus Question\n", 1032 | "We worked a lot with random data with numpy, but is there a way we can insure that we always get the same random numbers? What does the seed value mean? Does it matter what the actual number is? [Click Here for a Hint](https://www.google.com/search?q=numpy+random+seed)" 1033 | ] 1034 | }, 1035 | { 1036 | "cell_type": "code", 1037 | "execution_count": 11, 1038 | "metadata": {}, 1039 | "outputs": [ 1040 | { 1041 | "data": { 1042 | "text/plain": [ 1043 | "array([0.37454012, 0.95071431])" 1044 | ] 1045 | }, 1046 | "execution_count": 11, 1047 | "metadata": {}, 1048 | "output_type": "execute_result" 1049 | } 1050 | ], 1051 | "source": [ 1052 | "np.random.seed(42)\n", 1053 | "np.random.rand(2)" 1054 | ] 1055 | }, 1056 | { 1057 | "cell_type": "markdown", 1058 | "metadata": { 1059 | "collapsed": true, 1060 | "jupyter": { 1061 | "outputs_hidden": true 1062 | } 1063 | }, 1064 | "source": [ 1065 | "# Great Job!" 1066 | ] 1067 | } 1068 | ], 1069 | "metadata": { 1070 | "anaconda-cloud": {}, 1071 | "kernelspec": { 1072 | "display_name": "Python 3 (ipykernel)", 1073 | "language": "python", 1074 | "name": "python3" 1075 | }, 1076 | "language_info": { 1077 | "codemirror_mode": { 1078 | "name": "ipython", 1079 | "version": 3 1080 | }, 1081 | "file_extension": ".py", 1082 | "mimetype": "text/x-python", 1083 | "name": "python", 1084 | "nbconvert_exporter": "python", 1085 | "pygments_lexer": "ipython3", 1086 | "version": "3.8.10" 1087 | } 1088 | }, 1089 | "nbformat": 4, 1090 | "nbformat_minor": 4 1091 | } 1092 | -------------------------------------------------------------------------------- /Exercise Notebooks/.ipynb_checkpoints/Playing with numpy library-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 143, 6 | "id": "b9499a42-604e-41af-889d-140cec4baace", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 144, 16 | "id": "38d272a3-447f-4636-8341-76827f10a53a", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "first_array = np.array([[10,20,30],[40,50,60]])" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 145, 26 | "id": "454e89e1-c805-4845-9e84-0ae931dfd351", 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "[[10 20 30]\n", 34 | " [40 50 60]]\n" 35 | ] 36 | } 37 | ], 38 | "source": [ 39 | "print(first_array)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 146, 45 | "id": "087623e2-973f-4208-9335-5557b11d42c6", 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "name": "stdout", 50 | "output_type": "stream", 51 | "text": [ 52 | "[10 20 30]\n", 53 | "[40 50 60]\n" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "for i in first_array:\n", 59 | " print(i)" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 147, 65 | "id": "ce319ee7-24f1-4446-aaa8-90d8b548cefd", 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "10\n", 73 | "20\n", 74 | "30\n", 75 | "40\n", 76 | "50\n", 77 | "60\n" 78 | ] 79 | } 80 | ], 81 | "source": [ 82 | "for i in first_array:\n", 83 | " for j in i: \n", 84 | " print(j)" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 148, 90 | "id": "b6f37ae7-e18a-46ca-8945-16beed12a5cf", 91 | "metadata": {}, 92 | "outputs": [ 93 | { 94 | "data": { 95 | "text/plain": [ 96 | "numpy.ndarray" 97 | ] 98 | }, 99 | "execution_count": 148, 100 | "metadata": {}, 101 | "output_type": "execute_result" 102 | } 103 | ], 104 | "source": [ 105 | "type(first_array)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 149, 111 | "id": "8df5ae2f-4686-4ea3-a1c6-5399d4baa8f7", 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "second_array = np.array(range(100))" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 150, 121 | "id": "2fe72892-2c3c-4511-ac1c-3c448c5c06c5", 122 | "metadata": {}, 123 | "outputs": [ 124 | { 125 | "name": "stdout", 126 | "output_type": "stream", 127 | "text": [ 128 | "[[ 0 1 2 3 4 5 6 7 8 9]\n", 129 | " [10 11 12 13 14 15 16 17 18 19]\n", 130 | " [20 21 22 23 24 25 26 27 28 29]\n", 131 | " [30 31 32 33 34 35 36 37 38 39]\n", 132 | " [40 41 42 43 44 45 46 47 48 49]\n", 133 | " [50 51 52 53 54 55 56 57 58 59]\n", 134 | " [60 61 62 63 64 65 66 67 68 69]\n", 135 | " [70 71 72 73 74 75 76 77 78 79]\n", 136 | " [80 81 82 83 84 85 86 87 88 89]\n", 137 | " [90 91 92 93 94 95 96 97 98 99]]\n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "print(second_array.reshape(10,10))" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 151, 148 | "id": "063b49a8-c352-4551-8a27-27287ca1d8df", 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "third_array = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 152, 158 | "id": "75c84eba-6475-4b62-8268-448d1c54f44b", 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "name": "stdout", 163 | "output_type": "stream", 164 | "text": [ 165 | "0\n", 166 | "1\n", 167 | "2\n", 168 | "3\n", 169 | "4\n", 170 | "5\n", 171 | "6\n", 172 | "7\n" 173 | ] 174 | } 175 | ], 176 | "source": [ 177 | "for i in third_array:\n", 178 | " for j in i:\n", 179 | " for k in j:\n", 180 | " print(k)" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 153, 186 | "id": "a115da36-4f4f-46fa-acd0-e6b0adb34b3d", 187 | "metadata": {}, 188 | "outputs": [ 189 | { 190 | "name": "stdout", 191 | "output_type": "stream", 192 | "text": [ 193 | "0\n", 194 | "1\n", 195 | "2\n", 196 | "3\n", 197 | "4\n", 198 | "5\n", 199 | "6\n", 200 | "7\n" 201 | ] 202 | } 203 | ], 204 | "source": [ 205 | "for i in np.nditer(third_array):\n", 206 | " print(i)" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 154, 212 | "id": "743b8459-08b7-47e7-ad8b-d62a953ac6df", 213 | "metadata": {}, 214 | "outputs": [ 215 | { 216 | "name": "stdout", 217 | "output_type": "stream", 218 | "text": [ 219 | "(0, 0, 0) 0\n", 220 | "(0, 0, 1) 1\n", 221 | "(0, 1, 0) 2\n", 222 | "(0, 1, 1) 3\n", 223 | "(1, 0, 0) 4\n", 224 | "(1, 0, 1) 5\n", 225 | "(1, 1, 0) 6\n", 226 | "(1, 1, 1) 7\n" 227 | ] 228 | } 229 | ], 230 | "source": [ 231 | "# hey, binary to decimal system, I'm lucky :)\n", 232 | "for i,j in np.ndenumerate(third_array):\n", 233 | " print(i,j)" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": 155, 239 | "id": "9dfdf998-bc9f-420f-b9bb-32644c931ab7", 240 | "metadata": {}, 241 | "outputs": [], 242 | "source": [ 243 | "python_list = [[1,2] , [3,4] , [5,6]]" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 156, 249 | "id": "eaf4b23c-b1ab-4d0f-8b12-07a12cf5b8a5", 250 | "metadata": {}, 251 | "outputs": [ 252 | { 253 | "data": { 254 | "text/plain": [ 255 | "array([[1, 2],\n", 256 | " [3, 4],\n", 257 | " [5, 6]])" 258 | ] 259 | }, 260 | "execution_count": 156, 261 | "metadata": {}, 262 | "output_type": "execute_result" 263 | } 264 | ], 265 | "source": [ 266 | "np.array(python_list)" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 157, 272 | "id": "e56dbf39-4f4a-42ff-80fb-bb66001a4565", 273 | "metadata": {}, 274 | "outputs": [ 275 | { 276 | "data": { 277 | "text/plain": [ 278 | "array([ 0, 2, 4, 6, 8, 10])" 279 | ] 280 | }, 281 | "execution_count": 157, 282 | "metadata": {}, 283 | "output_type": "execute_result" 284 | } 285 | ], 286 | "source": [ 287 | "np.arange(0,11,2)" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 158, 293 | "id": "99989246-c773-4893-b9c6-1b4c9d27c2fe", 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" 300 | ] 301 | }, 302 | "execution_count": 158, 303 | "metadata": {}, 304 | "output_type": "execute_result" 305 | } 306 | ], 307 | "source": [ 308 | "np.zeros(10 , int)" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": 159, 314 | "id": "fca3b98f-0da3-4c82-ae40-300f132603e8", 315 | "metadata": {}, 316 | "outputs": [ 317 | { 318 | "data": { 319 | "text/plain": [ 320 | "array([[0, 0, 0],\n", 321 | " [0, 0, 0],\n", 322 | " [0, 0, 0]])" 323 | ] 324 | }, 325 | "execution_count": 159, 326 | "metadata": {}, 327 | "output_type": "execute_result" 328 | } 329 | ], 330 | "source": [ 331 | "np.zeros((3,3) , int)" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 160, 337 | "id": "e268c25d-4581-4441-9c4f-e956316e9bfc", 338 | "metadata": {}, 339 | "outputs": [ 340 | { 341 | "data": { 342 | "text/plain": [ 343 | "array([[[1., 1., 1., 1.],\n", 344 | " [1., 1., 1., 1.]],\n", 345 | "\n", 346 | " [[1., 1., 1., 1.],\n", 347 | " [1., 1., 1., 1.]],\n", 348 | "\n", 349 | " [[1., 1., 1., 1.],\n", 350 | " [1., 1., 1., 1.]],\n", 351 | "\n", 352 | " [[1., 1., 1., 1.],\n", 353 | " [1., 1., 1., 1.]],\n", 354 | "\n", 355 | " [[1., 1., 1., 1.],\n", 356 | " [1., 1., 1., 1.]]])" 357 | ] 358 | }, 359 | "execution_count": 160, 360 | "metadata": {}, 361 | "output_type": "execute_result" 362 | } 363 | ], 364 | "source": [ 365 | "np.ones((5,2,4))" 366 | ] 367 | }, 368 | { 369 | "cell_type": "code", 370 | "execution_count": 161, 371 | "id": "3b116d19-1650-46c5-8257-369c50b23cf2", 372 | "metadata": {}, 373 | "outputs": [ 374 | { 375 | "data": { 376 | "text/plain": [ 377 | "array([[1, 0, 0],\n", 378 | " [0, 1, 0],\n", 379 | " [0, 0, 1]])" 380 | ] 381 | }, 382 | "execution_count": 161, 383 | "metadata": {}, 384 | "output_type": "execute_result" 385 | } 386 | ], 387 | "source": [ 388 | "#diagonal matrices\n", 389 | "np.eye(3,dtype=int)" 390 | ] 391 | }, 392 | { 393 | "cell_type": "code", 394 | "execution_count": 162, 395 | "id": "14348881-36f8-4385-929a-767be93da987", 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "data": { 400 | "text/plain": [ 401 | "array([[0.52477466, 0.39986097, 0.04666566],\n", 402 | " [0.97375552, 0.23277134, 0.09060643],\n", 403 | " [0.61838601, 0.38246199, 0.98323089]])" 404 | ] 405 | }, 406 | "execution_count": 162, 407 | "metadata": {}, 408 | "output_type": "execute_result" 409 | } 410 | ], 411 | "source": [ 412 | "#between 0 and 1\n", 413 | "np.random.rand(3,3)" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 163, 419 | "id": "90199006-29a6-489c-ae4a-589652d6292d", 420 | "metadata": {}, 421 | "outputs": [ 422 | { 423 | "data": { 424 | "text/plain": [ 425 | "array([[ 1.13433927, -0.10474555, -0.52512285, 1.91277127],\n", 426 | " [-2.02671962, 1.11942361, 0.77919263, -1.10109776]])" 427 | ] 428 | }, 429 | "execution_count": 163, 430 | "metadata": {}, 431 | "output_type": "execute_result" 432 | } 433 | ], 434 | "source": [ 435 | "# standard distribution contain variance number(negetive)\n", 436 | "np.random.randn(2,4)" 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "execution_count": 164, 442 | "id": "47667242-00bb-40d2-b3a7-2632f4190950", 443 | "metadata": {}, 444 | "outputs": [ 445 | { 446 | "data": { 447 | "text/plain": [ 448 | "array([[43, 7, 46, 34, 13],\n", 449 | " [16, 35, 49, 39, 3],\n", 450 | " [ 1, 5, 41, 3, 28]])" 451 | ] 452 | }, 453 | "execution_count": 164, 454 | "metadata": {}, 455 | "output_type": "execute_result" 456 | } 457 | ], 458 | "source": [ 459 | "np.random.randint(0,50,(3,5))" 460 | ] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": 165, 465 | "id": "e099b25c-c09a-403a-ad76-260fb4ebf380", 466 | "metadata": {}, 467 | "outputs": [ 468 | { 469 | "data": { 470 | "text/plain": [ 471 | "array([51, 92, 14, 71, 60, 20, 82, 86, 74, 74])" 472 | ] 473 | }, 474 | "execution_count": 165, 475 | "metadata": {}, 476 | "output_type": "execute_result" 477 | } 478 | ], 479 | "source": [ 480 | "# but this section doesn't change any compile and default value with np.random.seed()\n", 481 | "# especially in machine learning algorithms\n", 482 | "np.random.seed(42)\n", 483 | "np.random.randint(0,100,10)" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": 166, 489 | "id": "33039e74-390f-4f16-a61e-e081aac46ead", 490 | "metadata": {}, 491 | "outputs": [ 492 | { 493 | "data": { 494 | "text/plain": [ 495 | "array([87, 99, 23, 2, 21, 52, 1, 87, 29, 37])" 496 | ] 497 | }, 498 | "execution_count": 166, 499 | "metadata": {}, 500 | "output_type": "execute_result" 501 | } 502 | ], 503 | "source": [ 504 | "# in any compile reproduce new value\n", 505 | "np.random.randint(0,100,10)" 506 | ] 507 | }, 508 | { 509 | "cell_type": "code", 510 | "execution_count": 167, 511 | "id": "1b0e1455-9b5b-4b9d-b813-0395cbdac563", 512 | "metadata": {}, 513 | "outputs": [], 514 | "source": [ 515 | "ex_arr = np.random.randint(0,100,10)" 516 | ] 517 | }, 518 | { 519 | "cell_type": "code", 520 | "execution_count": 168, 521 | "id": "12543062-2799-4d00-8280-3dbb20dc2345", 522 | "metadata": {}, 523 | "outputs": [ 524 | { 525 | "data": { 526 | "text/plain": [ 527 | "numpy.ndarray" 528 | ] 529 | }, 530 | "execution_count": 168, 531 | "metadata": {}, 532 | "output_type": "execute_result" 533 | } 534 | ], 535 | "source": [ 536 | "type(ex_arr)" 537 | ] 538 | }, 539 | { 540 | "cell_type": "code", 541 | "execution_count": 169, 542 | "id": "29f55460-b5f3-4b10-af0a-96086e975a57", 543 | "metadata": {}, 544 | "outputs": [ 545 | { 546 | "data": { 547 | "text/plain": [ 548 | "dtype('int32')" 549 | ] 550 | }, 551 | "execution_count": 169, 552 | "metadata": {}, 553 | "output_type": "execute_result" 554 | } 555 | ], 556 | "source": [ 557 | "ex_arr.dtype" 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": 170, 563 | "id": "4e4f3fa4-059e-43b7-ac97-fa60aa53ba6f", 564 | "metadata": {}, 565 | "outputs": [ 566 | { 567 | "data": { 568 | "text/plain": [ 569 | "(10,)" 570 | ] 571 | }, 572 | "execution_count": 170, 573 | "metadata": {}, 574 | "output_type": "execute_result" 575 | } 576 | ], 577 | "source": [ 578 | "ex_arr.shape" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": 171, 584 | "id": "7f1ffe8c-23a0-4d38-82d8-df14d8450f1a", 585 | "metadata": {}, 586 | "outputs": [ 587 | { 588 | "data": { 589 | "text/plain": [ 590 | "array([[ 1, 63, 59, 20, 32],\n", 591 | " [75, 57, 21, 88, 48]])" 592 | ] 593 | }, 594 | "execution_count": 171, 595 | "metadata": {}, 596 | "output_type": "execute_result" 597 | } 598 | ], 599 | "source": [ 600 | "ex_arr.reshape(2,5)" 601 | ] 602 | }, 603 | { 604 | "cell_type": "code", 605 | "execution_count": 172, 606 | "id": "adc377f2-3d76-430c-bb28-f30e99e6494f", 607 | "metadata": {}, 608 | "outputs": [ 609 | { 610 | "data": { 611 | "text/plain": [ 612 | "88" 613 | ] 614 | }, 615 | "execution_count": 172, 616 | "metadata": {}, 617 | "output_type": "execute_result" 618 | } 619 | ], 620 | "source": [ 621 | "ex_arr.max()" 622 | ] 623 | }, 624 | { 625 | "cell_type": "code", 626 | "execution_count": 173, 627 | "id": "63c679b1-d835-4235-906d-1394a39c2df5", 628 | "metadata": {}, 629 | "outputs": [ 630 | { 631 | "data": { 632 | "text/plain": [ 633 | "8" 634 | ] 635 | }, 636 | "execution_count": 173, 637 | "metadata": {}, 638 | "output_type": "execute_result" 639 | } 640 | ], 641 | "source": [ 642 | "ex_arr.argmax()" 643 | ] 644 | }, 645 | { 646 | "cell_type": "code", 647 | "execution_count": 174, 648 | "id": "01281f3f-5c31-400e-bd6d-8ef06210b964", 649 | "metadata": {}, 650 | "outputs": [], 651 | "source": [ 652 | "arr_2d = np.array([[1,2,3],[4,5,6],[7,8,9]])" 653 | ] 654 | }, 655 | { 656 | "cell_type": "code", 657 | "execution_count": 175, 658 | "id": "4acc3789-fcca-4b5c-85d8-e33468c908c1", 659 | "metadata": {}, 660 | "outputs": [ 661 | { 662 | "data": { 663 | "text/plain": [ 664 | "(3, 3)" 665 | ] 666 | }, 667 | "execution_count": 175, 668 | "metadata": {}, 669 | "output_type": "execute_result" 670 | } 671 | ], 672 | "source": [ 673 | "arr_2d.shape" 674 | ] 675 | }, 676 | { 677 | "cell_type": "code", 678 | "execution_count": 176, 679 | "id": "fb9f7640-d7f7-4706-bde2-c336c792ee5f", 680 | "metadata": {}, 681 | "outputs": [ 682 | { 683 | "data": { 684 | "text/plain": [ 685 | "array([[4, 5, 6]])" 686 | ] 687 | }, 688 | "execution_count": 176, 689 | "metadata": {}, 690 | "output_type": "execute_result" 691 | } 692 | ], 693 | "source": [ 694 | "arr_2d[1:2]" 695 | ] 696 | }, 697 | { 698 | "cell_type": "code", 699 | "execution_count": 177, 700 | "id": "7006585b-a31e-4bec-8504-672431ada2aa", 701 | "metadata": {}, 702 | "outputs": [], 703 | "source": [ 704 | "arr_copy = arr_2d.copy()" 705 | ] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": 178, 710 | "id": "50733767-33f8-4665-8340-3fef829da337", 711 | "metadata": {}, 712 | "outputs": [ 713 | { 714 | "data": { 715 | "text/plain": [ 716 | "array([[4, 5],\n", 717 | " [7, 8]])" 718 | ] 719 | }, 720 | "execution_count": 178, 721 | "metadata": {}, 722 | "output_type": "execute_result" 723 | } 724 | ], 725 | "source": [ 726 | "arr_copy[1:,:2]" 727 | ] 728 | }, 729 | { 730 | "cell_type": "code", 731 | "execution_count": 179, 732 | "id": "df1c8e69-8a8f-4c4a-9335-4eac25fe8bde", 733 | "metadata": {}, 734 | "outputs": [], 735 | "source": [ 736 | "arr_bool = arr_copy > 4" 737 | ] 738 | }, 739 | { 740 | "cell_type": "code", 741 | "execution_count": 180, 742 | "id": "c9efd0d5-d984-4f91-b2d2-a46049540e07", 743 | "metadata": {}, 744 | "outputs": [ 745 | { 746 | "data": { 747 | "text/plain": [ 748 | "array([5, 6, 7, 8, 9])" 749 | ] 750 | }, 751 | "execution_count": 180, 752 | "metadata": {}, 753 | "output_type": "execute_result" 754 | } 755 | ], 756 | "source": [ 757 | "arr_copy[arr_bool]" 758 | ] 759 | }, 760 | { 761 | "cell_type": "code", 762 | "execution_count": 185, 763 | "id": "4c753625-9fed-4846-a8db-fe8ab190eaae", 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "45" 770 | ] 771 | }, 772 | "execution_count": 185, 773 | "metadata": {}, 774 | "output_type": "execute_result" 775 | } 776 | ], 777 | "source": [ 778 | "arr_copy.sum()" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": 186, 784 | "id": "eb4be50c-2fe0-4a58-81c0-54156e9aeb1d", 785 | "metadata": {}, 786 | "outputs": [ 787 | { 788 | "data": { 789 | "text/plain": [ 790 | "5.0" 791 | ] 792 | }, 793 | "execution_count": 186, 794 | "metadata": {}, 795 | "output_type": "execute_result" 796 | } 797 | ], 798 | "source": [ 799 | "arr_copy.mean()" 800 | ] 801 | }, 802 | { 803 | "cell_type": "code", 804 | "execution_count": 187, 805 | "id": "279c5632-78aa-481c-b26e-54d4b1679a2c", 806 | "metadata": {}, 807 | "outputs": [], 808 | "source": [ 809 | "arr_5_5 = np.arange(25).reshape(5,5)" 810 | ] 811 | }, 812 | { 813 | "cell_type": "code", 814 | "execution_count": 188, 815 | "id": "36d0b85e-30cf-471a-a032-cb0a2ebc5c66", 816 | "metadata": {}, 817 | "outputs": [ 818 | { 819 | "data": { 820 | "text/plain": [ 821 | "array([[ 0, 1, 2, 3, 4],\n", 822 | " [ 5, 6, 7, 8, 9],\n", 823 | " [10, 11, 12, 13, 14],\n", 824 | " [15, 16, 17, 18, 19],\n", 825 | " [20, 21, 22, 23, 24]])" 826 | ] 827 | }, 828 | "execution_count": 188, 829 | "metadata": {}, 830 | "output_type": "execute_result" 831 | } 832 | ], 833 | "source": [ 834 | "arr_5_5" 835 | ] 836 | }, 837 | { 838 | "cell_type": "code", 839 | "execution_count": 190, 840 | "id": "0ef86885-5728-456b-a970-8d5cb2531147", 841 | "metadata": {}, 842 | "outputs": [ 843 | { 844 | "data": { 845 | "text/plain": [ 846 | "array([50, 55, 60, 65, 70])" 847 | ] 848 | }, 849 | "execution_count": 190, 850 | "metadata": {}, 851 | "output_type": "execute_result" 852 | } 853 | ], 854 | "source": [ 855 | "# column by column \n", 856 | "arr_5_5.sum(axis = 0)" 857 | ] 858 | }, 859 | { 860 | "cell_type": "code", 861 | "execution_count": 191, 862 | "id": "c0f647f1-4f5d-43e0-b67d-4cf99c9ede17", 863 | "metadata": {}, 864 | "outputs": [ 865 | { 866 | "data": { 867 | "text/plain": [ 868 | "array([ 10, 35, 60, 85, 110])" 869 | ] 870 | }, 871 | "execution_count": 191, 872 | "metadata": {}, 873 | "output_type": "execute_result" 874 | } 875 | ], 876 | "source": [ 877 | "# row by row\n", 878 | "arr_5_5.sum(axis = 1)" 879 | ] 880 | }, 881 | { 882 | "cell_type": "code", 883 | "execution_count": 192, 884 | "id": "057bff41-68bc-4f28-876f-f724e251ade3", 885 | "metadata": {}, 886 | "outputs": [ 887 | { 888 | "data": { 889 | "text/plain": [ 890 | "array([ 2., 7., 12., 17., 22.])" 891 | ] 892 | }, 893 | "execution_count": 192, 894 | "metadata": {}, 895 | "output_type": "execute_result" 896 | } 897 | ], 898 | "source": [ 899 | "# average of first row \n", 900 | "arr_5_5.mean(axis=1)" 901 | ] 902 | }, 903 | { 904 | "cell_type": "code", 905 | "execution_count": null, 906 | "id": "bbac4955-4e1b-47ff-9419-40f7804ef064", 907 | "metadata": {}, 908 | "outputs": [], 909 | "source": [] 910 | } 911 | ], 912 | "metadata": { 913 | "kernelspec": { 914 | "display_name": "Python 3 (ipykernel)", 915 | "language": "python", 916 | "name": "python3" 917 | }, 918 | "language_info": { 919 | "codemirror_mode": { 920 | "name": "ipython", 921 | "version": 3 922 | }, 923 | "file_extension": ".py", 924 | "mimetype": "text/x-python", 925 | "name": "python", 926 | "nbconvert_exporter": "python", 927 | "pygments_lexer": "ipython3", 928 | "version": "3.8.10" 929 | } 930 | }, 931 | "nbformat": 4, 932 | "nbformat_minor": 5 933 | } 934 | -------------------------------------------------------------------------------- /Exercise Notebooks/.ipynb_checkpoints/Playing_With_Numpy_Library-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "b9499a42-604e-41af-889d-140cec4baace", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "id": "38d272a3-447f-4636-8341-76827f10a53a", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "first_array = np.array([[10,20,30],[40,50,60]])" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "id": "454e89e1-c805-4845-9e84-0ae931dfd351", 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "[[10 20 30]\n", 34 | " [40 50 60]]\n" 35 | ] 36 | } 37 | ], 38 | "source": [ 39 | "print(first_array)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 4, 45 | "id": "087623e2-973f-4208-9335-5557b11d42c6", 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "name": "stdout", 50 | "output_type": "stream", 51 | "text": [ 52 | "[10 20 30]\n", 53 | "[40 50 60]\n" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "for i in first_array:\n", 59 | " print(i)" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 5, 65 | "id": "ce319ee7-24f1-4446-aaa8-90d8b548cefd", 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "10\n", 73 | "20\n", 74 | "30\n", 75 | "40\n", 76 | "50\n", 77 | "60\n" 78 | ] 79 | } 80 | ], 81 | "source": [ 82 | "for i in first_array:\n", 83 | " for j in i: \n", 84 | " print(j)" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 6, 90 | "id": "b6f37ae7-e18a-46ca-8945-16beed12a5cf", 91 | "metadata": {}, 92 | "outputs": [ 93 | { 94 | "data": { 95 | "text/plain": [ 96 | "numpy.ndarray" 97 | ] 98 | }, 99 | "execution_count": 6, 100 | "metadata": {}, 101 | "output_type": "execute_result" 102 | } 103 | ], 104 | "source": [ 105 | "type(first_array)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 7, 111 | "id": "8df5ae2f-4686-4ea3-a1c6-5399d4baa8f7", 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "second_array = np.array(range(100))" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 8, 121 | "id": "2fe72892-2c3c-4511-ac1c-3c448c5c06c5", 122 | "metadata": {}, 123 | "outputs": [ 124 | { 125 | "name": "stdout", 126 | "output_type": "stream", 127 | "text": [ 128 | "[[ 0 1 2 3 4 5 6 7 8 9]\n", 129 | " [10 11 12 13 14 15 16 17 18 19]\n", 130 | " [20 21 22 23 24 25 26 27 28 29]\n", 131 | " [30 31 32 33 34 35 36 37 38 39]\n", 132 | " [40 41 42 43 44 45 46 47 48 49]\n", 133 | " [50 51 52 53 54 55 56 57 58 59]\n", 134 | " [60 61 62 63 64 65 66 67 68 69]\n", 135 | " [70 71 72 73 74 75 76 77 78 79]\n", 136 | " [80 81 82 83 84 85 86 87 88 89]\n", 137 | " [90 91 92 93 94 95 96 97 98 99]]\n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "print(second_array.reshape(10,10))" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 9, 148 | "id": "063b49a8-c352-4551-8a27-27287ca1d8df", 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "third_array = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 10, 158 | "id": "75c84eba-6475-4b62-8268-448d1c54f44b", 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "name": "stdout", 163 | "output_type": "stream", 164 | "text": [ 165 | "0\n", 166 | "1\n", 167 | "2\n", 168 | "3\n", 169 | "4\n", 170 | "5\n", 171 | "6\n", 172 | "7\n" 173 | ] 174 | } 175 | ], 176 | "source": [ 177 | "for i in third_array:\n", 178 | " for j in i:\n", 179 | " for k in j:\n", 180 | " print(k)" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 11, 186 | "id": "a115da36-4f4f-46fa-acd0-e6b0adb34b3d", 187 | "metadata": {}, 188 | "outputs": [ 189 | { 190 | "name": "stdout", 191 | "output_type": "stream", 192 | "text": [ 193 | "0\n", 194 | "1\n", 195 | "2\n", 196 | "3\n", 197 | "4\n", 198 | "5\n", 199 | "6\n", 200 | "7\n" 201 | ] 202 | } 203 | ], 204 | "source": [ 205 | "for i in np.nditer(third_array):\n", 206 | " print(i)" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 12, 212 | "id": "743b8459-08b7-47e7-ad8b-d62a953ac6df", 213 | "metadata": {}, 214 | "outputs": [ 215 | { 216 | "name": "stdout", 217 | "output_type": "stream", 218 | "text": [ 219 | "(0, 0, 0) 0\n", 220 | "(0, 0, 1) 1\n", 221 | "(0, 1, 0) 2\n", 222 | "(0, 1, 1) 3\n", 223 | "(1, 0, 0) 4\n", 224 | "(1, 0, 1) 5\n", 225 | "(1, 1, 0) 6\n", 226 | "(1, 1, 1) 7\n" 227 | ] 228 | } 229 | ], 230 | "source": [ 231 | "# hey, binary to decimal system, I'm lucky :)\n", 232 | "for i,j in np.ndenumerate(third_array):\n", 233 | " print(i,j)" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": 13, 239 | "id": "9dfdf998-bc9f-420f-b9bb-32644c931ab7", 240 | "metadata": {}, 241 | "outputs": [], 242 | "source": [ 243 | "python_list = [[1,2] , [3,4] , [5,6]]" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 14, 249 | "id": "eaf4b23c-b1ab-4d0f-8b12-07a12cf5b8a5", 250 | "metadata": {}, 251 | "outputs": [ 252 | { 253 | "data": { 254 | "text/plain": [ 255 | "array([[1, 2],\n", 256 | " [3, 4],\n", 257 | " [5, 6]])" 258 | ] 259 | }, 260 | "execution_count": 14, 261 | "metadata": {}, 262 | "output_type": "execute_result" 263 | } 264 | ], 265 | "source": [ 266 | "np.array(python_list)" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 15, 272 | "id": "e56dbf39-4f4a-42ff-80fb-bb66001a4565", 273 | "metadata": {}, 274 | "outputs": [ 275 | { 276 | "data": { 277 | "text/plain": [ 278 | "array([ 0, 2, 4, 6, 8, 10])" 279 | ] 280 | }, 281 | "execution_count": 15, 282 | "metadata": {}, 283 | "output_type": "execute_result" 284 | } 285 | ], 286 | "source": [ 287 | "np.arange(0,11,2)" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 16, 293 | "id": "99989246-c773-4893-b9c6-1b4c9d27c2fe", 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" 300 | ] 301 | }, 302 | "execution_count": 16, 303 | "metadata": {}, 304 | "output_type": "execute_result" 305 | } 306 | ], 307 | "source": [ 308 | "np.zeros(10 , int)" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": 17, 314 | "id": "fca3b98f-0da3-4c82-ae40-300f132603e8", 315 | "metadata": {}, 316 | "outputs": [ 317 | { 318 | "data": { 319 | "text/plain": [ 320 | "array([[0, 0, 0],\n", 321 | " [0, 0, 0],\n", 322 | " [0, 0, 0]])" 323 | ] 324 | }, 325 | "execution_count": 17, 326 | "metadata": {}, 327 | "output_type": "execute_result" 328 | } 329 | ], 330 | "source": [ 331 | "np.zeros((3,3) , int)" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 18, 337 | "id": "e268c25d-4581-4441-9c4f-e956316e9bfc", 338 | "metadata": {}, 339 | "outputs": [ 340 | { 341 | "data": { 342 | "text/plain": [ 343 | "array([[[1., 1., 1., 1.],\n", 344 | " [1., 1., 1., 1.]],\n", 345 | "\n", 346 | " [[1., 1., 1., 1.],\n", 347 | " [1., 1., 1., 1.]],\n", 348 | "\n", 349 | " [[1., 1., 1., 1.],\n", 350 | " [1., 1., 1., 1.]],\n", 351 | "\n", 352 | " [[1., 1., 1., 1.],\n", 353 | " [1., 1., 1., 1.]],\n", 354 | "\n", 355 | " [[1., 1., 1., 1.],\n", 356 | " [1., 1., 1., 1.]]])" 357 | ] 358 | }, 359 | "execution_count": 18, 360 | "metadata": {}, 361 | "output_type": "execute_result" 362 | } 363 | ], 364 | "source": [ 365 | "np.ones((5,2,4))" 366 | ] 367 | }, 368 | { 369 | "cell_type": "code", 370 | "execution_count": 19, 371 | "id": "3b116d19-1650-46c5-8257-369c50b23cf2", 372 | "metadata": {}, 373 | "outputs": [ 374 | { 375 | "data": { 376 | "text/plain": [ 377 | "array([[1, 0, 0],\n", 378 | " [0, 1, 0],\n", 379 | " [0, 0, 1]])" 380 | ] 381 | }, 382 | "execution_count": 19, 383 | "metadata": {}, 384 | "output_type": "execute_result" 385 | } 386 | ], 387 | "source": [ 388 | "#diagonal matrices\n", 389 | "np.eye(3,dtype=int)" 390 | ] 391 | }, 392 | { 393 | "cell_type": "code", 394 | "execution_count": 20, 395 | "id": "14348881-36f8-4385-929a-767be93da987", 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "data": { 400 | "text/plain": [ 401 | "array([[0.23125289, 0.17657115, 0.73858594],\n", 402 | " [0.34483192, 0.40058807, 0.39550327],\n", 403 | " [0.37153114, 0.40700279, 0.77381529]])" 404 | ] 405 | }, 406 | "execution_count": 20, 407 | "metadata": {}, 408 | "output_type": "execute_result" 409 | } 410 | ], 411 | "source": [ 412 | "#between 0 and 1\n", 413 | "np.random.rand(3,3)" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 21, 419 | "id": "90199006-29a6-489c-ae4a-589652d6292d", 420 | "metadata": {}, 421 | "outputs": [ 422 | { 423 | "data": { 424 | "text/plain": [ 425 | "array([[ 0.43468629, 0.77698343, -0.26364651, 0.71574969],\n", 426 | " [-0.4778537 , -1.68156296, 0.85724506, 0.54020524]])" 427 | ] 428 | }, 429 | "execution_count": 21, 430 | "metadata": {}, 431 | "output_type": "execute_result" 432 | } 433 | ], 434 | "source": [ 435 | "# standard distribution contain variance number(negetive)\n", 436 | "np.random.randn(2,4)" 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "execution_count": 22, 442 | "id": "47667242-00bb-40d2-b3a7-2632f4190950", 443 | "metadata": {}, 444 | "outputs": [ 445 | { 446 | "data": { 447 | "text/plain": [ 448 | "array([[35, 47, 26, 4, 48],\n", 449 | " [42, 25, 16, 27, 10],\n", 450 | " [35, 27, 17, 31, 18]])" 451 | ] 452 | }, 453 | "execution_count": 22, 454 | "metadata": {}, 455 | "output_type": "execute_result" 456 | } 457 | ], 458 | "source": [ 459 | "np.random.randint(0,50,(3,5))" 460 | ] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": 23, 465 | "id": "e099b25c-c09a-403a-ad76-260fb4ebf380", 466 | "metadata": {}, 467 | "outputs": [ 468 | { 469 | "data": { 470 | "text/plain": [ 471 | "array([51, 92, 14, 71, 60, 20, 82, 86, 74, 74])" 472 | ] 473 | }, 474 | "execution_count": 23, 475 | "metadata": {}, 476 | "output_type": "execute_result" 477 | } 478 | ], 479 | "source": [ 480 | "# but this section doesn't change any compile and default value with np.random.seed()\n", 481 | "# especially in machine learning algorithms\n", 482 | "np.random.seed(42)\n", 483 | "np.random.randint(0,100,10)" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": 24, 489 | "id": "33039e74-390f-4f16-a61e-e081aac46ead", 490 | "metadata": {}, 491 | "outputs": [ 492 | { 493 | "data": { 494 | "text/plain": [ 495 | "array([87, 99, 23, 2, 21, 52, 1, 87, 29, 37])" 496 | ] 497 | }, 498 | "execution_count": 24, 499 | "metadata": {}, 500 | "output_type": "execute_result" 501 | } 502 | ], 503 | "source": [ 504 | "# in any compile reproduce new value\n", 505 | "np.random.randint(0,100,10)" 506 | ] 507 | }, 508 | { 509 | "cell_type": "code", 510 | "execution_count": 25, 511 | "id": "1b0e1455-9b5b-4b9d-b813-0395cbdac563", 512 | "metadata": {}, 513 | "outputs": [], 514 | "source": [ 515 | "ex_arr = np.random.randint(0,100,10)" 516 | ] 517 | }, 518 | { 519 | "cell_type": "code", 520 | "execution_count": 26, 521 | "id": "12543062-2799-4d00-8280-3dbb20dc2345", 522 | "metadata": {}, 523 | "outputs": [ 524 | { 525 | "data": { 526 | "text/plain": [ 527 | "numpy.ndarray" 528 | ] 529 | }, 530 | "execution_count": 26, 531 | "metadata": {}, 532 | "output_type": "execute_result" 533 | } 534 | ], 535 | "source": [ 536 | "type(ex_arr)" 537 | ] 538 | }, 539 | { 540 | "cell_type": "code", 541 | "execution_count": 27, 542 | "id": "29f55460-b5f3-4b10-af0a-96086e975a57", 543 | "metadata": {}, 544 | "outputs": [ 545 | { 546 | "data": { 547 | "text/plain": [ 548 | "dtype('int32')" 549 | ] 550 | }, 551 | "execution_count": 27, 552 | "metadata": {}, 553 | "output_type": "execute_result" 554 | } 555 | ], 556 | "source": [ 557 | "ex_arr.dtype" 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": 28, 563 | "id": "4e4f3fa4-059e-43b7-ac97-fa60aa53ba6f", 564 | "metadata": {}, 565 | "outputs": [ 566 | { 567 | "data": { 568 | "text/plain": [ 569 | "(10,)" 570 | ] 571 | }, 572 | "execution_count": 28, 573 | "metadata": {}, 574 | "output_type": "execute_result" 575 | } 576 | ], 577 | "source": [ 578 | "ex_arr.shape" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": 29, 584 | "id": "7f1ffe8c-23a0-4d38-82d8-df14d8450f1a", 585 | "metadata": {}, 586 | "outputs": [ 587 | { 588 | "data": { 589 | "text/plain": [ 590 | "array([[ 1, 63, 59, 20, 32],\n", 591 | " [75, 57, 21, 88, 48]])" 592 | ] 593 | }, 594 | "execution_count": 29, 595 | "metadata": {}, 596 | "output_type": "execute_result" 597 | } 598 | ], 599 | "source": [ 600 | "ex_arr.reshape(2,5)" 601 | ] 602 | }, 603 | { 604 | "cell_type": "code", 605 | "execution_count": 30, 606 | "id": "adc377f2-3d76-430c-bb28-f30e99e6494f", 607 | "metadata": {}, 608 | "outputs": [ 609 | { 610 | "data": { 611 | "text/plain": [ 612 | "88" 613 | ] 614 | }, 615 | "execution_count": 30, 616 | "metadata": {}, 617 | "output_type": "execute_result" 618 | } 619 | ], 620 | "source": [ 621 | "ex_arr.max()" 622 | ] 623 | }, 624 | { 625 | "cell_type": "code", 626 | "execution_count": 31, 627 | "id": "63c679b1-d835-4235-906d-1394a39c2df5", 628 | "metadata": {}, 629 | "outputs": [ 630 | { 631 | "data": { 632 | "text/plain": [ 633 | "8" 634 | ] 635 | }, 636 | "execution_count": 31, 637 | "metadata": {}, 638 | "output_type": "execute_result" 639 | } 640 | ], 641 | "source": [ 642 | "ex_arr.argmax()" 643 | ] 644 | }, 645 | { 646 | "cell_type": "code", 647 | "execution_count": 32, 648 | "id": "01281f3f-5c31-400e-bd6d-8ef06210b964", 649 | "metadata": {}, 650 | "outputs": [], 651 | "source": [ 652 | "arr_2d = np.array([[1,2,3],[4,5,6],[7,8,9]])" 653 | ] 654 | }, 655 | { 656 | "cell_type": "code", 657 | "execution_count": 33, 658 | "id": "4acc3789-fcca-4b5c-85d8-e33468c908c1", 659 | "metadata": {}, 660 | "outputs": [ 661 | { 662 | "data": { 663 | "text/plain": [ 664 | "(3, 3)" 665 | ] 666 | }, 667 | "execution_count": 33, 668 | "metadata": {}, 669 | "output_type": "execute_result" 670 | } 671 | ], 672 | "source": [ 673 | "arr_2d.shape" 674 | ] 675 | }, 676 | { 677 | "cell_type": "code", 678 | "execution_count": 34, 679 | "id": "fb9f7640-d7f7-4706-bde2-c336c792ee5f", 680 | "metadata": {}, 681 | "outputs": [ 682 | { 683 | "data": { 684 | "text/plain": [ 685 | "array([[4, 5, 6]])" 686 | ] 687 | }, 688 | "execution_count": 34, 689 | "metadata": {}, 690 | "output_type": "execute_result" 691 | } 692 | ], 693 | "source": [ 694 | "arr_2d[1:2]" 695 | ] 696 | }, 697 | { 698 | "cell_type": "code", 699 | "execution_count": 35, 700 | "id": "7006585b-a31e-4bec-8504-672431ada2aa", 701 | "metadata": {}, 702 | "outputs": [], 703 | "source": [ 704 | "arr_copy = arr_2d.copy()" 705 | ] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": 36, 710 | "id": "50733767-33f8-4665-8340-3fef829da337", 711 | "metadata": {}, 712 | "outputs": [ 713 | { 714 | "data": { 715 | "text/plain": [ 716 | "array([[4, 5],\n", 717 | " [7, 8]])" 718 | ] 719 | }, 720 | "execution_count": 36, 721 | "metadata": {}, 722 | "output_type": "execute_result" 723 | } 724 | ], 725 | "source": [ 726 | "arr_copy[1:,:2]" 727 | ] 728 | }, 729 | { 730 | "cell_type": "code", 731 | "execution_count": 37, 732 | "id": "df1c8e69-8a8f-4c4a-9335-4eac25fe8bde", 733 | "metadata": {}, 734 | "outputs": [], 735 | "source": [ 736 | "arr_bool = arr_copy > 4" 737 | ] 738 | }, 739 | { 740 | "cell_type": "code", 741 | "execution_count": 38, 742 | "id": "c9efd0d5-d984-4f91-b2d2-a46049540e07", 743 | "metadata": {}, 744 | "outputs": [ 745 | { 746 | "data": { 747 | "text/plain": [ 748 | "array([5, 6, 7, 8, 9])" 749 | ] 750 | }, 751 | "execution_count": 38, 752 | "metadata": {}, 753 | "output_type": "execute_result" 754 | } 755 | ], 756 | "source": [ 757 | "arr_copy[arr_bool]" 758 | ] 759 | }, 760 | { 761 | "cell_type": "code", 762 | "execution_count": 39, 763 | "id": "4c753625-9fed-4846-a8db-fe8ab190eaae", 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "45" 770 | ] 771 | }, 772 | "execution_count": 39, 773 | "metadata": {}, 774 | "output_type": "execute_result" 775 | } 776 | ], 777 | "source": [ 778 | "arr_copy.sum()" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": 40, 784 | "id": "eb4be50c-2fe0-4a58-81c0-54156e9aeb1d", 785 | "metadata": {}, 786 | "outputs": [ 787 | { 788 | "data": { 789 | "text/plain": [ 790 | "5.0" 791 | ] 792 | }, 793 | "execution_count": 40, 794 | "metadata": {}, 795 | "output_type": "execute_result" 796 | } 797 | ], 798 | "source": [ 799 | "arr_copy.mean()" 800 | ] 801 | }, 802 | { 803 | "cell_type": "code", 804 | "execution_count": 41, 805 | "id": "279c5632-78aa-481c-b26e-54d4b1679a2c", 806 | "metadata": {}, 807 | "outputs": [], 808 | "source": [ 809 | "arr_5_5 = np.arange(25).reshape(5,5)" 810 | ] 811 | }, 812 | { 813 | "cell_type": "code", 814 | "execution_count": 42, 815 | "id": "36d0b85e-30cf-471a-a032-cb0a2ebc5c66", 816 | "metadata": {}, 817 | "outputs": [ 818 | { 819 | "data": { 820 | "text/plain": [ 821 | "array([[ 0, 1, 2, 3, 4],\n", 822 | " [ 5, 6, 7, 8, 9],\n", 823 | " [10, 11, 12, 13, 14],\n", 824 | " [15, 16, 17, 18, 19],\n", 825 | " [20, 21, 22, 23, 24]])" 826 | ] 827 | }, 828 | "execution_count": 42, 829 | "metadata": {}, 830 | "output_type": "execute_result" 831 | } 832 | ], 833 | "source": [ 834 | "arr_5_5" 835 | ] 836 | }, 837 | { 838 | "cell_type": "code", 839 | "execution_count": 43, 840 | "id": "0ef86885-5728-456b-a970-8d5cb2531147", 841 | "metadata": {}, 842 | "outputs": [ 843 | { 844 | "data": { 845 | "text/plain": [ 846 | "array([50, 55, 60, 65, 70])" 847 | ] 848 | }, 849 | "execution_count": 43, 850 | "metadata": {}, 851 | "output_type": "execute_result" 852 | } 853 | ], 854 | "source": [ 855 | "# column by column \n", 856 | "arr_5_5.sum(axis = 0)" 857 | ] 858 | }, 859 | { 860 | "cell_type": "code", 861 | "execution_count": 44, 862 | "id": "c0f647f1-4f5d-43e0-b67d-4cf99c9ede17", 863 | "metadata": {}, 864 | "outputs": [ 865 | { 866 | "data": { 867 | "text/plain": [ 868 | "array([ 10, 35, 60, 85, 110])" 869 | ] 870 | }, 871 | "execution_count": 44, 872 | "metadata": {}, 873 | "output_type": "execute_result" 874 | } 875 | ], 876 | "source": [ 877 | "# row by row\n", 878 | "arr_5_5.sum(axis = 1)" 879 | ] 880 | }, 881 | { 882 | "cell_type": "code", 883 | "execution_count": 45, 884 | "id": "057bff41-68bc-4f28-876f-f724e251ade3", 885 | "metadata": {}, 886 | "outputs": [ 887 | { 888 | "data": { 889 | "text/plain": [ 890 | "array([ 2., 7., 12., 17., 22.])" 891 | ] 892 | }, 893 | "execution_count": 45, 894 | "metadata": {}, 895 | "output_type": "execute_result" 896 | } 897 | ], 898 | "source": [ 899 | "# average of first row \n", 900 | "arr_5_5.mean(axis=1)" 901 | ] 902 | }, 903 | { 904 | "cell_type": "code", 905 | "execution_count": 48, 906 | "id": "bbac4955-4e1b-47ff-9419-40f7804ef064", 907 | "metadata": {}, 908 | "outputs": [ 909 | { 910 | "data": { 911 | "text/plain": [ 912 | "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" 913 | ] 914 | }, 915 | "execution_count": 48, 916 | "metadata": {}, 917 | "output_type": "execute_result" 918 | } 919 | ], 920 | "source": [ 921 | "# use in neural networks to flat the array \n", 922 | "arr_2d.flatten()" 923 | ] 924 | }, 925 | { 926 | "cell_type": "code", 927 | "execution_count": 51, 928 | "id": "6206f7a9-d696-4ae6-bea5-6ec11165ee28", 929 | "metadata": {}, 930 | "outputs": [ 931 | { 932 | "data": { 933 | "text/plain": [ 934 | "array([[1, 2, 3],\n", 935 | " [4, 5, 6],\n", 936 | " [7, 8, 9]])" 937 | ] 938 | }, 939 | "execution_count": 51, 940 | "metadata": {}, 941 | "output_type": "execute_result" 942 | } 943 | ], 944 | "source": [ 945 | "arr_2d" 946 | ] 947 | }, 948 | { 949 | "cell_type": "code", 950 | "execution_count": 53, 951 | "id": "9e912888-02c1-4549-b84f-ee4c99a55225", 952 | "metadata": {}, 953 | "outputs": [ 954 | { 955 | "data": { 956 | "text/plain": [ 957 | "array([7, 8, 9])" 958 | ] 959 | }, 960 | "execution_count": 53, 961 | "metadata": {}, 962 | "output_type": "execute_result" 963 | } 964 | ], 965 | "source": [ 966 | "arr_2d.max(axis=0)" 967 | ] 968 | }, 969 | { 970 | "cell_type": "code", 971 | "execution_count": 55, 972 | "id": "9cca3470-c8fa-4719-9897-2b46e198fb47", 973 | "metadata": {}, 974 | "outputs": [ 975 | { 976 | "data": { 977 | "text/plain": [ 978 | "array([1, 4, 7])" 979 | ] 980 | }, 981 | "execution_count": 55, 982 | "metadata": {}, 983 | "output_type": "execute_result" 984 | } 985 | ], 986 | "source": [ 987 | "arr_2d.min(axis=1)" 988 | ] 989 | }, 990 | { 991 | "cell_type": "code", 992 | "execution_count": 58, 993 | "id": "5448699e-68fd-4478-b023-d938f8c99625", 994 | "metadata": {}, 995 | "outputs": [ 996 | { 997 | "data": { 998 | "text/plain": [ 999 | "array([[0, 0, 0],\n", 1000 | " [0, 1, 1],\n", 1001 | " [1, 1, 1]])" 1002 | ] 1003 | }, 1004 | "execution_count": 58, 1005 | "metadata": {}, 1006 | "output_type": "execute_result" 1007 | } 1008 | ], 1009 | "source": [ 1010 | "# condition in array use np.where \n", 1011 | "np.where(arr_2d >= 5 , 1 , 0)" 1012 | ] 1013 | }, 1014 | { 1015 | "cell_type": "code", 1016 | "execution_count": 73, 1017 | "id": "1d6157c6-e908-4dba-8c97-10ec4e7edb53", 1018 | "metadata": {}, 1019 | "outputs": [], 1020 | "source": [ 1021 | "np.random.shuffle(arr_5_5)\n", 1022 | "np.random.seed(55)" 1023 | ] 1024 | }, 1025 | { 1026 | "cell_type": "code", 1027 | "execution_count": 74, 1028 | "id": "e483a583-ba09-4509-8766-a1dca3c61f46", 1029 | "metadata": {}, 1030 | "outputs": [ 1031 | { 1032 | "data": { 1033 | "text/plain": [ 1034 | "array([[20, 21, 22, 23, 24],\n", 1035 | " [ 5, 6, 7, 8, 9],\n", 1036 | " [15, 16, 17, 18, 19],\n", 1037 | " [10, 11, 12, 13, 14],\n", 1038 | " [ 0, 1, 2, 3, 4]])" 1039 | ] 1040 | }, 1041 | "execution_count": 74, 1042 | "metadata": {}, 1043 | "output_type": "execute_result" 1044 | } 1045 | ], 1046 | "source": [ 1047 | "arr_5_5" 1048 | ] 1049 | }, 1050 | { 1051 | "cell_type": "code", 1052 | "execution_count": 83, 1053 | "id": "80241cb5-638a-4f07-be24-eb37d5ea4277", 1054 | "metadata": {}, 1055 | "outputs": [ 1056 | { 1057 | "data": { 1058 | "text/plain": [ 1059 | "array([8, 1, 5, 0, 7, 2, 9, 4, 3, 6])" 1060 | ] 1061 | }, 1062 | "execution_count": 83, 1063 | "metadata": {}, 1064 | "output_type": "execute_result" 1065 | } 1066 | ], 1067 | "source": [ 1068 | "np.random.seed(42)\n", 1069 | "np.random.permutation(10)" 1070 | ] 1071 | }, 1072 | { 1073 | "cell_type": "code", 1074 | "execution_count": 85, 1075 | "id": "f5024991-565c-4aef-a7ad-903bc6825b84", 1076 | "metadata": {}, 1077 | "outputs": [ 1078 | { 1079 | "data": { 1080 | "text/plain": [ 1081 | "array([[20, 21, 22, 23, 24],\n", 1082 | " [ 5, 6, 7, 8, 9],\n", 1083 | " [15, 16, 17, 18, 19],\n", 1084 | " [10, 11, 12, 13, 14],\n", 1085 | " [ 0, 1, 2, 3, 4]])" 1086 | ] 1087 | }, 1088 | "execution_count": 85, 1089 | "metadata": {}, 1090 | "output_type": "execute_result" 1091 | } 1092 | ], 1093 | "source": [ 1094 | "np.random.permutation(arr_5_5)" 1095 | ] 1096 | }, 1097 | { 1098 | "cell_type": "code", 1099 | "execution_count": 104, 1100 | "id": "ac266379-2e8e-42d7-a5ce-492bc9da32f9", 1101 | "metadata": {}, 1102 | "outputs": [ 1103 | { 1104 | "data": { 1105 | "text/plain": [ 1106 | "[6, 7, 5]" 1107 | ] 1108 | }, 1109 | "execution_count": 104, 1110 | "metadata": {}, 1111 | "output_type": "execute_result" 1112 | } 1113 | ], 1114 | "source": [ 1115 | "from random import sample \n", 1116 | "sample([2,5,6,7,8,9], 3)\n" 1117 | ] 1118 | } 1119 | ], 1120 | "metadata": { 1121 | "kernelspec": { 1122 | "display_name": "Python 3 (ipykernel)", 1123 | "language": "python", 1124 | "name": "python3" 1125 | }, 1126 | "language_info": { 1127 | "codemirror_mode": { 1128 | "name": "ipython", 1129 | "version": 3 1130 | }, 1131 | "file_extension": ".py", 1132 | "mimetype": "text/x-python", 1133 | "name": "python", 1134 | "nbconvert_exporter": "python", 1135 | "pygments_lexer": "ipython3", 1136 | "version": "3.8.10" 1137 | } 1138 | }, 1139 | "nbformat": 4, 1140 | "nbformat_minor": 5 1141 | } 1142 | -------------------------------------------------------------------------------- /Exercise Notebooks/.ipynb_checkpoints/Training_a_Convolutional_Layer_With_Recognition_Model_Using_Pytorch_on_the_MNIST_Dataset-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 5 6 | } 7 | -------------------------------------------------------------------------------- /Exercise Notebooks/Data/Data_model_CIFAR-10.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YasinRezvani/Hands_On_Machine_Learning_and_Deep_Learning_with_Python/0f1876aa3e18636857a38aac46fc2f0843a009f8/Exercise Notebooks/Data/Data_model_CIFAR-10.h5 -------------------------------------------------------------------------------- /Exercise Notebooks/Data/Data_model_CIFAR-10.json: -------------------------------------------------------------------------------- 1 | {"class_name": "Sequential", "config": {"name": "sequential_12", 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43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /Exercise Notebooks/Data/iris.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3.0,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5.0,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5.0,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3.0,1.4,0.1,setosa 15 | 4.3,3.0,1.1,0.1,setosa 16 | 5.8,4.0,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1.0,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5.0,3.0,1.6,0.2,setosa 28 | 5.0,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.2,setosa 37 | 5.0,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.6,1.4,0.1,setosa 40 | 4.4,3.0,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5.0,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5.0,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3.0,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5.0,3.3,1.4,0.2,setosa 52 | 7.0,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4.0,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1.0,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5.0,2.0,3.5,1.0,versicolor 63 | 5.9,3.0,4.2,1.5,versicolor 64 | 6.0,2.2,4.0,1.0,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3.0,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1.0,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4.0,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3.0,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3.0,5.0,1.7,versicolor 80 | 6.0,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1.0,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1.0,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6.0,2.7,5.1,1.6,versicolor 86 | 5.4,3.0,4.5,1.5,versicolor 87 | 6.0,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3.0,4.1,1.3,versicolor 91 | 5.5,2.5,4.0,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3.0,4.6,1.4,versicolor 94 | 5.8,2.6,4.0,1.2,versicolor 95 | 5.0,2.3,3.3,1.0,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3.0,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3.0,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6.0,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3.0,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3.0,5.8,2.2,virginica 107 | 7.6,3.0,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2.0,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3.0,5.5,2.1,virginica 115 | 5.7,2.5,5.0,2.0,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3.0,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6.0,2.2,5.0,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2.0,virginica 124 | 7.7,2.8,6.7,2.0,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6.0,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3.0,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3.0,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2.0,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3.0,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6.0,3.0,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3.0,5.2,2.3,virginica 148 | 6.3,2.5,5.0,1.9,virginica 149 | 6.5,3.0,5.2,2.0,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3.0,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- /Exercise Notebooks/Data/model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YasinRezvani/Hands_On_Machine_Learning_and_Deep_Learning_with_Python/0f1876aa3e18636857a38aac46fc2f0843a009f8/Exercise Notebooks/Data/model.h5 -------------------------------------------------------------------------------- /Exercise Notebooks/Data/model.json: -------------------------------------------------------------------------------- 1 | {"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 28, 28], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_2"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "registered_name": null, "build_config": {"input_shape": [null, 28, 28]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 10, "activation": "softmax", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 784]}}]}, "keras_version": "2.13.1", "backend": "tensorflow"} -------------------------------------------------------------------------------- /Exercise Notebooks/Data/outputfromDataFrame.csv: -------------------------------------------------------------------------------- 1 | W,E,R,T,new columns 2 | 0.0,-75.0,-42,-32,37 3 | 0.0,35.0,58,-73,-47 4 | 0.0,-20.0,-46,28,49 5 | 26.0,-20.0,-34,6,-22 6 | -------------------------------------------------------------------------------- /Exercise Notebooks/Data/outputfromDataFramewithindex.csv: -------------------------------------------------------------------------------- 1 | States,W,E,R,T,new columns 2 | Bastam,0.0,-75.0,-42,-32,37 3 | Shahrood,0.0,35.0,58,-73,-47 4 | Bonab,0.0,-20.0,-46,28,49 5 | Tabriz,26.0,-20.0,-34,6,-22 6 | -------------------------------------------------------------------------------- /Exercise Notebooks/NumPy_Exercises_From_Udemy_Course.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "___\n", 8 | "\n", 9 | "\n", 10 | "___\n", 11 | "
Copyright Pierian Data
\n", 12 | "
For more information, visit us at www.pieriandata.com
" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "# NumPy Exercises\n", 20 | "\n", 21 | "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks and then you'll be asked some more complicated questions.\n", 22 | "\n", 23 | "
IMPORTANT NOTE! Make sure you don't run the cells directly above the example output shown,
otherwise you will end up writing over the example output!
" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "#### 1. Import NumPy as np" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 2, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "import numpy as np" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "#### 2. Create an array of 10 zeros " 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 2, 52 | "metadata": {}, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" 58 | ] 59 | }, 60 | "execution_count": 2, 61 | "metadata": {}, 62 | "output_type": "execute_result" 63 | } 64 | ], 65 | "source": [ 66 | "# CODE HERE\n", 67 | "np.zeros(10)" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 2, 73 | "metadata": {}, 74 | "outputs": [ 75 | { 76 | "data": { 77 | "text/plain": [ 78 | "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" 79 | ] 80 | }, 81 | "execution_count": 2, 82 | "metadata": {}, 83 | "output_type": "execute_result" 84 | } 85 | ], 86 | "source": [ 87 | "# DON'T WRITE HERE" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "#### 3. Create an array of 10 ones" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 3, 100 | "metadata": {}, 101 | "outputs": [ 102 | { 103 | "data": { 104 | "text/plain": [ 105 | "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" 106 | ] 107 | }, 108 | "execution_count": 3, 109 | "metadata": {}, 110 | "output_type": "execute_result" 111 | } 112 | ], 113 | "source": [ 114 | "np.ones(10)" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 3, 120 | "metadata": {}, 121 | "outputs": [ 122 | { 123 | "data": { 124 | "text/plain": [ 125 | "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" 126 | ] 127 | }, 128 | "execution_count": 3, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "# DON'T WRITE HERE" 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "metadata": {}, 140 | "source": [ 141 | "#### 4. Create an array of 10 fives" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 7, 147 | "metadata": {}, 148 | "outputs": [ 149 | { 150 | "data": { 151 | "text/plain": [ 152 | "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" 153 | ] 154 | }, 155 | "execution_count": 7, 156 | "metadata": {}, 157 | "output_type": "execute_result" 158 | } 159 | ], 160 | "source": [ 161 | "np.zeros(10) + 5" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": 4, 167 | "metadata": {}, 168 | "outputs": [ 169 | { 170 | "data": { 171 | "text/plain": [ 172 | "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" 173 | ] 174 | }, 175 | "execution_count": 4, 176 | "metadata": {}, 177 | "output_type": "execute_result" 178 | } 179 | ], 180 | "source": [ 181 | "# DON'T WRITE HERE" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "#### 5. Create an array of the integers from 10 to 50" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 8, 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "data": { 198 | "text/plain": [ 199 | "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", 200 | " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n", 201 | " 44, 45, 46, 47, 48, 49, 50])" 202 | ] 203 | }, 204 | "execution_count": 8, 205 | "metadata": {}, 206 | "output_type": "execute_result" 207 | } 208 | ], 209 | "source": [ 210 | "np.arange(10,51)" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 5, 216 | "metadata": {}, 217 | "outputs": [ 218 | { 219 | "data": { 220 | "text/plain": [ 221 | "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", 222 | " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n", 223 | " 44, 45, 46, 47, 48, 49, 50])" 224 | ] 225 | }, 226 | "execution_count": 5, 227 | "metadata": {}, 228 | "output_type": "execute_result" 229 | } 230 | ], 231 | "source": [ 232 | "# DON'T WRITE HERE" 233 | ] 234 | }, 235 | { 236 | "cell_type": "markdown", 237 | "metadata": {}, 238 | "source": [ 239 | "#### 6. Create an array of all the even integers from 10 to 50" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": 9, 245 | "metadata": {}, 246 | "outputs": [ 247 | { 248 | "data": { 249 | "text/plain": [ 250 | "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n", 251 | " 44, 46, 48, 50])" 252 | ] 253 | }, 254 | "execution_count": 9, 255 | "metadata": {}, 256 | "output_type": "execute_result" 257 | } 258 | ], 259 | "source": [ 260 | "np.arange(10,51,2)" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 6, 266 | "metadata": {}, 267 | "outputs": [ 268 | { 269 | "data": { 270 | "text/plain": [ 271 | "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n", 272 | " 44, 46, 48, 50])" 273 | ] 274 | }, 275 | "execution_count": 6, 276 | "metadata": {}, 277 | "output_type": "execute_result" 278 | } 279 | ], 280 | "source": [ 281 | "# DON'T WRITE HERE" 282 | ] 283 | }, 284 | { 285 | "cell_type": "markdown", 286 | "metadata": {}, 287 | "source": [ 288 | "#### 7. Create a 3x3 matrix with values ranging from 0 to 8" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 11, 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "array([[0, 1, 2],\n", 300 | " [3, 4, 5],\n", 301 | " [6, 7, 8]])" 302 | ] 303 | }, 304 | "execution_count": 11, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | } 308 | ], 309 | "source": [ 310 | "np.arange(0,9).reshape(3,3)" 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": 7, 316 | "metadata": {}, 317 | "outputs": [ 318 | { 319 | "data": { 320 | "text/plain": [ 321 | "array([[0, 1, 2],\n", 322 | " [3, 4, 5],\n", 323 | " [6, 7, 8]])" 324 | ] 325 | }, 326 | "execution_count": 7, 327 | "metadata": {}, 328 | "output_type": "execute_result" 329 | } 330 | ], 331 | "source": [ 332 | "# DON'T WRITE HERE" 333 | ] 334 | }, 335 | { 336 | "cell_type": "markdown", 337 | "metadata": {}, 338 | "source": [ 339 | "#### 8. Create a 3x3 identity matrix" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": 12, 345 | "metadata": {}, 346 | "outputs": [ 347 | { 348 | "data": { 349 | "text/plain": [ 350 | "array([[1., 0., 0.],\n", 351 | " [0., 1., 0.],\n", 352 | " [0., 0., 1.]])" 353 | ] 354 | }, 355 | "execution_count": 12, 356 | "metadata": {}, 357 | "output_type": "execute_result" 358 | } 359 | ], 360 | "source": [ 361 | "np.eye(3,3)" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 8, 367 | "metadata": {}, 368 | "outputs": [ 369 | { 370 | "data": { 371 | "text/plain": [ 372 | "array([[1., 0., 0.],\n", 373 | " [0., 1., 0.],\n", 374 | " [0., 0., 1.]])" 375 | ] 376 | }, 377 | "execution_count": 8, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "# DON'T WRITE HERE" 384 | ] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": {}, 389 | "source": [ 390 | "#### 9. Use NumPy to generate a random number between 0 and 1

 NOTE: Your result's value should be different from the one shown below." 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 30, 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "data": { 400 | "text/plain": [ 401 | "array([0.37454012])" 402 | ] 403 | }, 404 | "execution_count": 30, 405 | "metadata": {}, 406 | "output_type": "execute_result" 407 | } 408 | ], 409 | "source": [ 410 | "np.random.seed(42)\n", 411 | "np.random.rand(1)" 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "execution_count": 28, 417 | "metadata": {}, 418 | "outputs": [], 419 | "source": [ 420 | "# DON'T WRITE HERE" 421 | ] 422 | }, 423 | { 424 | "cell_type": "markdown", 425 | "metadata": {}, 426 | "source": [ 427 | "#### 10. Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution

  NOTE: Your result's values should be different from the ones shown below." 428 | ] 429 | }, 430 | { 431 | "cell_type": "code", 432 | "execution_count": 33, 433 | "metadata": {}, 434 | "outputs": [ 435 | { 436 | "data": { 437 | "text/plain": [ 438 | "array([ 0.49671415, -0.1382643 , 0.64768854, 1.52302986, -0.23415337,\n", 439 | " -0.23413696, 1.57921282, 0.76743473, -0.46947439, 0.54256004,\n", 440 | " -0.46341769, -0.46572975, 0.24196227, -1.91328024, -1.72491783,\n", 441 | " -0.56228753, -1.01283112, 0.31424733, -0.90802408, -1.4123037 ,\n", 442 | " 1.46564877, -0.2257763 , 0.0675282 , -1.42474819, -0.54438272])" 443 | ] 444 | }, 445 | "execution_count": 33, 446 | "metadata": {}, 447 | "output_type": "execute_result" 448 | } 449 | ], 450 | "source": [ 451 | "np.random.seed(42)\n", 452 | "np.random.randn(25)" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 10, 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "data": { 462 | "text/plain": [ 463 | "array([ 1.80076712, -1.12375847, -0.98524305, 0.11673573, 1.96346762,\n", 464 | " 1.81378592, -0.33790771, 0.85012656, 0.0100703 , -0.91005957,\n", 465 | " 0.29064366, 0.69906357, 0.1774377 , -0.61958694, -0.45498611,\n", 466 | " -2.0804685 , -0.06778549, 1.06403819, 0.4311884 , -1.09853837,\n", 467 | " 1.11980469, -0.48751963, 1.32517611, -0.61775122, -0.00622865])" 468 | ] 469 | }, 470 | "execution_count": 10, 471 | "metadata": {}, 472 | "output_type": "execute_result" 473 | } 474 | ], 475 | "source": [ 476 | "# DON'T WRITE HERE" 477 | ] 478 | }, 479 | { 480 | "cell_type": "markdown", 481 | "metadata": {}, 482 | "source": [ 483 | "#### 11. Create the following matrix:" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": 5, 489 | "metadata": {}, 490 | "outputs": [ 491 | { 492 | "data": { 493 | "text/plain": [ 494 | "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", 495 | " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", 496 | " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", 497 | " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", 498 | " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", 499 | " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", 500 | " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", 501 | " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", 502 | " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", 503 | " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" 504 | ] 505 | }, 506 | "execution_count": 5, 507 | "metadata": {}, 508 | "output_type": "execute_result" 509 | } 510 | ], 511 | "source": [ 512 | "np.arange(0.01,1.01,0.01,dtype=float).reshape(10,10)\n", 513 | "np.arange(1,101).reshape(10,10) / 100" 514 | ] 515 | }, 516 | { 517 | "cell_type": "code", 518 | "execution_count": 11, 519 | "metadata": {}, 520 | "outputs": [ 521 | { 522 | "data": { 523 | "text/plain": [ 524 | "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", 525 | " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", 526 | " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", 527 | " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", 528 | " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", 529 | " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", 530 | " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", 531 | " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", 532 | " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", 533 | " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" 534 | ] 535 | }, 536 | "execution_count": 11, 537 | "metadata": {}, 538 | "output_type": "execute_result" 539 | } 540 | ], 541 | "source": [ 542 | "# DON'T WRITE HERE" 543 | ] 544 | }, 545 | { 546 | "cell_type": "markdown", 547 | "metadata": {}, 548 | "source": [ 549 | "#### 12. Create an array of 20 linearly spaced points between 0 and 1:" 550 | ] 551 | }, 552 | { 553 | "cell_type": "code", 554 | "execution_count": 46, 555 | "metadata": {}, 556 | "outputs": [ 557 | { 558 | "data": { 559 | "text/plain": [ 560 | "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", 561 | " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", 562 | " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", 563 | " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" 564 | ] 565 | }, 566 | "execution_count": 46, 567 | "metadata": {}, 568 | "output_type": "execute_result" 569 | } 570 | ], 571 | "source": [ 572 | "np.linspace(0,1,20)" 573 | ] 574 | }, 575 | { 576 | "cell_type": "code", 577 | "execution_count": 12, 578 | "metadata": {}, 579 | "outputs": [ 580 | { 581 | "data": { 582 | "text/plain": [ 583 | "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", 584 | " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", 585 | " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", 586 | " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" 587 | ] 588 | }, 589 | "execution_count": 12, 590 | "metadata": {}, 591 | "output_type": "execute_result" 592 | } 593 | ], 594 | "source": [ 595 | "# DON'T WRITE HERE" 596 | ] 597 | }, 598 | { 599 | "cell_type": "markdown", 600 | "metadata": {}, 601 | "source": [ 602 | "## Numpy Indexing and Selection\n", 603 | "\n", 604 | "Now you will be given a starting matrix (be sure to run the cell below!), and be asked to replicate the resulting matrix outputs:" 605 | ] 606 | }, 607 | { 608 | "cell_type": "code", 609 | "execution_count": 48, 610 | "metadata": {}, 611 | "outputs": [ 612 | { 613 | "data": { 614 | "text/plain": [ 615 | "array([[ 1, 2, 3, 4, 5],\n", 616 | " [ 6, 7, 8, 9, 10],\n", 617 | " [11, 12, 13, 14, 15],\n", 618 | " [16, 17, 18, 19, 20],\n", 619 | " [21, 22, 23, 24, 25]])" 620 | ] 621 | }, 622 | "execution_count": 48, 623 | "metadata": {}, 624 | "output_type": "execute_result" 625 | } 626 | ], 627 | "source": [ 628 | "# RUN THIS CELL - THIS IS OUR STARTING MATRIX\n", 629 | "mat = np.arange(1,26).reshape(5,5)\n", 630 | "mat" 631 | ] 632 | }, 633 | { 634 | "cell_type": "markdown", 635 | "metadata": {}, 636 | "source": [ 637 | "#### 13. Write code that reproduces the output shown below.

  Be careful not to run the cell immediately above the output, otherwise you won't be able to see the output any more." 638 | ] 639 | }, 640 | { 641 | "cell_type": "code", 642 | "execution_count": 56, 643 | "metadata": {}, 644 | "outputs": [ 645 | { 646 | "data": { 647 | "text/plain": [ 648 | "array([[12, 13, 14, 15],\n", 649 | " [17, 18, 19, 20],\n", 650 | " [22, 23, 24, 25]])" 651 | ] 652 | }, 653 | "execution_count": 56, 654 | "metadata": {}, 655 | "output_type": "execute_result" 656 | } 657 | ], 658 | "source": [ 659 | "# CODE HERE\n", 660 | "mat[2:,1:]" 661 | ] 662 | }, 663 | { 664 | "cell_type": "code", 665 | "execution_count": 14, 666 | "metadata": {}, 667 | "outputs": [ 668 | { 669 | "data": { 670 | "text/plain": [ 671 | "array([[12, 13, 14, 15],\n", 672 | " [17, 18, 19, 20],\n", 673 | " [22, 23, 24, 25]])" 674 | ] 675 | }, 676 | "execution_count": 14, 677 | "metadata": {}, 678 | "output_type": "execute_result" 679 | } 680 | ], 681 | "source": [ 682 | "# DON'T WRITE HERE" 683 | ] 684 | }, 685 | { 686 | "cell_type": "markdown", 687 | "metadata": {}, 688 | "source": [ 689 | "#### 14. Write code that reproduces the output shown below." 690 | ] 691 | }, 692 | { 693 | "cell_type": "code", 694 | "execution_count": 57, 695 | "metadata": {}, 696 | "outputs": [ 697 | { 698 | "data": { 699 | "text/plain": [ 700 | "20" 701 | ] 702 | }, 703 | "execution_count": 57, 704 | "metadata": {}, 705 | "output_type": "execute_result" 706 | } 707 | ], 708 | "source": [ 709 | "mat[3,4]" 710 | ] 711 | }, 712 | { 713 | "cell_type": "code", 714 | "execution_count": 15, 715 | "metadata": {}, 716 | "outputs": [ 717 | { 718 | "data": { 719 | "text/plain": [ 720 | "20" 721 | ] 722 | }, 723 | "execution_count": 15, 724 | "metadata": {}, 725 | "output_type": "execute_result" 726 | } 727 | ], 728 | "source": [ 729 | "# DON'T WRITE HERE" 730 | ] 731 | }, 732 | { 733 | "cell_type": "markdown", 734 | "metadata": {}, 735 | "source": [ 736 | "#### 15. Write code that reproduces the output shown below." 737 | ] 738 | }, 739 | { 740 | "cell_type": "code", 741 | "execution_count": 58, 742 | "metadata": {}, 743 | "outputs": [ 744 | { 745 | "data": { 746 | "text/plain": [ 747 | "array([[ 2],\n", 748 | " [ 7],\n", 749 | " [12]])" 750 | ] 751 | }, 752 | "execution_count": 58, 753 | "metadata": {}, 754 | "output_type": "execute_result" 755 | } 756 | ], 757 | "source": [ 758 | "mat[:3,1:2]" 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": 16, 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "array([[ 2],\n", 770 | " [ 7],\n", 771 | " [12]])" 772 | ] 773 | }, 774 | "execution_count": 16, 775 | "metadata": {}, 776 | "output_type": "execute_result" 777 | } 778 | ], 779 | "source": [ 780 | "# DON'T WRITE HERE" 781 | ] 782 | }, 783 | { 784 | "cell_type": "markdown", 785 | "metadata": {}, 786 | "source": [ 787 | "#### 16. Write code that reproduces the output shown below." 788 | ] 789 | }, 790 | { 791 | "cell_type": "code", 792 | "execution_count": 61, 793 | "metadata": {}, 794 | "outputs": [ 795 | { 796 | "data": { 797 | "text/plain": [ 798 | "array([21, 22, 23, 24, 25])" 799 | ] 800 | }, 801 | "execution_count": 61, 802 | "metadata": {}, 803 | "output_type": "execute_result" 804 | } 805 | ], 806 | "source": [ 807 | "mat[4]" 808 | ] 809 | }, 810 | { 811 | "cell_type": "code", 812 | "execution_count": 17, 813 | "metadata": {}, 814 | "outputs": [ 815 | { 816 | "data": { 817 | "text/plain": [ 818 | "array([21, 22, 23, 24, 25])" 819 | ] 820 | }, 821 | "execution_count": 17, 822 | "metadata": {}, 823 | "output_type": "execute_result" 824 | } 825 | ], 826 | "source": [ 827 | "# DON'T WRITE HERE" 828 | ] 829 | }, 830 | { 831 | "cell_type": "markdown", 832 | "metadata": {}, 833 | "source": [ 834 | "#### 17. Write code that reproduces the output shown below." 835 | ] 836 | }, 837 | { 838 | "cell_type": "code", 839 | "execution_count": 62, 840 | "metadata": {}, 841 | "outputs": [ 842 | { 843 | "data": { 844 | "text/plain": [ 845 | "array([[16, 17, 18, 19, 20],\n", 846 | " [21, 22, 23, 24, 25]])" 847 | ] 848 | }, 849 | "execution_count": 62, 850 | "metadata": {}, 851 | "output_type": "execute_result" 852 | } 853 | ], 854 | "source": [ 855 | "mat[3:,:]" 856 | ] 857 | }, 858 | { 859 | "cell_type": "code", 860 | "execution_count": 18, 861 | "metadata": {}, 862 | "outputs": [ 863 | { 864 | "data": { 865 | "text/plain": [ 866 | "array([[16, 17, 18, 19, 20],\n", 867 | " [21, 22, 23, 24, 25]])" 868 | ] 869 | }, 870 | "execution_count": 18, 871 | "metadata": {}, 872 | "output_type": "execute_result" 873 | } 874 | ], 875 | "source": [ 876 | "# DON'T WRITE HERE" 877 | ] 878 | }, 879 | { 880 | "cell_type": "markdown", 881 | "metadata": {}, 882 | "source": [ 883 | "## NumPy Operations" 884 | ] 885 | }, 886 | { 887 | "cell_type": "markdown", 888 | "metadata": {}, 889 | "source": [ 890 | "#### 18. Get the sum of all the values in mat" 891 | ] 892 | }, 893 | { 894 | "cell_type": "code", 895 | "execution_count": 63, 896 | "metadata": {}, 897 | "outputs": [ 898 | { 899 | "data": { 900 | "text/plain": [ 901 | "325" 902 | ] 903 | }, 904 | "execution_count": 63, 905 | "metadata": {}, 906 | "output_type": "execute_result" 907 | } 908 | ], 909 | "source": [ 910 | "mat.sum()" 911 | ] 912 | }, 913 | { 914 | "cell_type": "code", 915 | "execution_count": 19, 916 | "metadata": {}, 917 | "outputs": [ 918 | { 919 | "data": { 920 | "text/plain": [ 921 | "325" 922 | ] 923 | }, 924 | "execution_count": 19, 925 | "metadata": {}, 926 | "output_type": "execute_result" 927 | } 928 | ], 929 | "source": [ 930 | "# DON'T WRITE HERE" 931 | ] 932 | }, 933 | { 934 | "cell_type": "markdown", 935 | "metadata": {}, 936 | "source": [ 937 | "#### 19. Get the standard deviation of the values in mat" 938 | ] 939 | }, 940 | { 941 | "cell_type": "code", 942 | "execution_count": 65, 943 | "metadata": {}, 944 | "outputs": [ 945 | { 946 | "data": { 947 | "text/plain": [ 948 | "7.211102550927978" 949 | ] 950 | }, 951 | "execution_count": 65, 952 | "metadata": {}, 953 | "output_type": "execute_result" 954 | } 955 | ], 956 | "source": [ 957 | "mat.std()" 958 | ] 959 | }, 960 | { 961 | "cell_type": "code", 962 | "execution_count": 20, 963 | "metadata": {}, 964 | "outputs": [ 965 | { 966 | "data": { 967 | "text/plain": [ 968 | "7.211102550927978" 969 | ] 970 | }, 971 | "execution_count": 20, 972 | "metadata": {}, 973 | "output_type": "execute_result" 974 | } 975 | ], 976 | "source": [ 977 | "# DON'T WRITE HERE" 978 | ] 979 | }, 980 | { 981 | "cell_type": "markdown", 982 | "metadata": {}, 983 | "source": [ 984 | "#### 20. Get the sum of all the columns in mat" 985 | ] 986 | }, 987 | { 988 | "cell_type": "code", 989 | "execution_count": 66, 990 | "metadata": {}, 991 | "outputs": [ 992 | { 993 | "data": { 994 | "text/plain": [ 995 | "array([55, 60, 65, 70, 75])" 996 | ] 997 | }, 998 | "execution_count": 66, 999 | "metadata": {}, 1000 | "output_type": "execute_result" 1001 | } 1002 | ], 1003 | "source": [ 1004 | "mat.sum(axis=0)" 1005 | ] 1006 | }, 1007 | { 1008 | "cell_type": "code", 1009 | "execution_count": 21, 1010 | "metadata": {}, 1011 | "outputs": [ 1012 | { 1013 | "data": { 1014 | "text/plain": [ 1015 | "array([55, 60, 65, 70, 75])" 1016 | ] 1017 | }, 1018 | "execution_count": 21, 1019 | "metadata": {}, 1020 | "output_type": "execute_result" 1021 | } 1022 | ], 1023 | "source": [ 1024 | "# DON'T WRITE HERE" 1025 | ] 1026 | }, 1027 | { 1028 | "cell_type": "markdown", 1029 | "metadata": {}, 1030 | "source": [ 1031 | "## Bonus Question\n", 1032 | "We worked a lot with random data with numpy, but is there a way we can insure that we always get the same random numbers? What does the seed value mean? Does it matter what the actual number is? [Click Here for a Hint](https://www.google.com/search?q=numpy+random+seed)" 1033 | ] 1034 | }, 1035 | { 1036 | "cell_type": "code", 1037 | "execution_count": 11, 1038 | "metadata": {}, 1039 | "outputs": [ 1040 | { 1041 | "data": { 1042 | "text/plain": [ 1043 | "array([0.37454012, 0.95071431])" 1044 | ] 1045 | }, 1046 | "execution_count": 11, 1047 | "metadata": {}, 1048 | "output_type": "execute_result" 1049 | } 1050 | ], 1051 | "source": [ 1052 | "np.random.seed(42)\n", 1053 | "np.random.rand(2)" 1054 | ] 1055 | }, 1056 | { 1057 | "cell_type": "markdown", 1058 | "metadata": { 1059 | "collapsed": true, 1060 | "jupyter": { 1061 | "outputs_hidden": true 1062 | } 1063 | }, 1064 | "source": [ 1065 | "# Great Job!" 1066 | ] 1067 | } 1068 | ], 1069 | "metadata": { 1070 | "anaconda-cloud": {}, 1071 | "kernelspec": { 1072 | "display_name": "Python 3 (ipykernel)", 1073 | "language": "python", 1074 | "name": "python3" 1075 | }, 1076 | "language_info": { 1077 | "codemirror_mode": { 1078 | "name": "ipython", 1079 | "version": 3 1080 | }, 1081 | "file_extension": ".py", 1082 | "mimetype": "text/x-python", 1083 | "name": "python", 1084 | "nbconvert_exporter": "python", 1085 | "pygments_lexer": "ipython3", 1086 | "version": "3.8.10" 1087 | } 1088 | }, 1089 | "nbformat": 4, 1090 | "nbformat_minor": 4 1091 | } 1092 | -------------------------------------------------------------------------------- /Exercise Notebooks/Playing_With_Numpy_Library.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "b9499a42-604e-41af-889d-140cec4baace", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "id": "38d272a3-447f-4636-8341-76827f10a53a", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "first_array = np.array([[10,20,30],[40,50,60]])" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "id": "454e89e1-c805-4845-9e84-0ae931dfd351", 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "[[10 20 30]\n", 34 | " [40 50 60]]\n" 35 | ] 36 | } 37 | ], 38 | "source": [ 39 | "print(first_array)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 4, 45 | "id": "087623e2-973f-4208-9335-5557b11d42c6", 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "name": "stdout", 50 | "output_type": "stream", 51 | "text": [ 52 | "[10 20 30]\n", 53 | "[40 50 60]\n" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "for i in first_array:\n", 59 | " print(i)" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 5, 65 | "id": "ce319ee7-24f1-4446-aaa8-90d8b548cefd", 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "10\n", 73 | "20\n", 74 | "30\n", 75 | "40\n", 76 | "50\n", 77 | "60\n" 78 | ] 79 | } 80 | ], 81 | "source": [ 82 | "for i in first_array:\n", 83 | " for j in i: \n", 84 | " print(j)" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 6, 90 | "id": "b6f37ae7-e18a-46ca-8945-16beed12a5cf", 91 | "metadata": {}, 92 | "outputs": [ 93 | { 94 | "data": { 95 | "text/plain": [ 96 | "numpy.ndarray" 97 | ] 98 | }, 99 | "execution_count": 6, 100 | "metadata": {}, 101 | "output_type": "execute_result" 102 | } 103 | ], 104 | "source": [ 105 | "type(first_array)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 7, 111 | "id": "8df5ae2f-4686-4ea3-a1c6-5399d4baa8f7", 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "second_array = np.array(range(100))" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 8, 121 | "id": "2fe72892-2c3c-4511-ac1c-3c448c5c06c5", 122 | "metadata": {}, 123 | "outputs": [ 124 | { 125 | "name": "stdout", 126 | "output_type": "stream", 127 | "text": [ 128 | "[[ 0 1 2 3 4 5 6 7 8 9]\n", 129 | " [10 11 12 13 14 15 16 17 18 19]\n", 130 | " [20 21 22 23 24 25 26 27 28 29]\n", 131 | " [30 31 32 33 34 35 36 37 38 39]\n", 132 | " [40 41 42 43 44 45 46 47 48 49]\n", 133 | " [50 51 52 53 54 55 56 57 58 59]\n", 134 | " [60 61 62 63 64 65 66 67 68 69]\n", 135 | " [70 71 72 73 74 75 76 77 78 79]\n", 136 | " [80 81 82 83 84 85 86 87 88 89]\n", 137 | " [90 91 92 93 94 95 96 97 98 99]]\n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "print(second_array.reshape(10,10))" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 9, 148 | "id": "063b49a8-c352-4551-8a27-27287ca1d8df", 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "third_array = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 10, 158 | "id": "75c84eba-6475-4b62-8268-448d1c54f44b", 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "name": "stdout", 163 | "output_type": "stream", 164 | "text": [ 165 | "0\n", 166 | "1\n", 167 | "2\n", 168 | "3\n", 169 | "4\n", 170 | "5\n", 171 | "6\n", 172 | "7\n" 173 | ] 174 | } 175 | ], 176 | "source": [ 177 | "for i in third_array:\n", 178 | " for j in i:\n", 179 | " for k in j:\n", 180 | " print(k)" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 11, 186 | "id": "a115da36-4f4f-46fa-acd0-e6b0adb34b3d", 187 | "metadata": {}, 188 | "outputs": [ 189 | { 190 | "name": "stdout", 191 | "output_type": "stream", 192 | "text": [ 193 | "0\n", 194 | "1\n", 195 | "2\n", 196 | "3\n", 197 | "4\n", 198 | "5\n", 199 | "6\n", 200 | "7\n" 201 | ] 202 | } 203 | ], 204 | "source": [ 205 | "for i in np.nditer(third_array):\n", 206 | " print(i)" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 12, 212 | "id": "743b8459-08b7-47e7-ad8b-d62a953ac6df", 213 | "metadata": {}, 214 | "outputs": [ 215 | { 216 | "name": "stdout", 217 | "output_type": "stream", 218 | "text": [ 219 | "(0, 0, 0) 0\n", 220 | "(0, 0, 1) 1\n", 221 | "(0, 1, 0) 2\n", 222 | "(0, 1, 1) 3\n", 223 | "(1, 0, 0) 4\n", 224 | "(1, 0, 1) 5\n", 225 | "(1, 1, 0) 6\n", 226 | "(1, 1, 1) 7\n" 227 | ] 228 | } 229 | ], 230 | "source": [ 231 | "# hey, binary to decimal system, I'm lucky :)\n", 232 | "for i,j in np.ndenumerate(third_array):\n", 233 | " print(i,j)" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": 13, 239 | "id": "9dfdf998-bc9f-420f-b9bb-32644c931ab7", 240 | "metadata": {}, 241 | "outputs": [], 242 | "source": [ 243 | "python_list = [[1,2] , [3,4] , [5,6]]" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 14, 249 | "id": "eaf4b23c-b1ab-4d0f-8b12-07a12cf5b8a5", 250 | "metadata": {}, 251 | "outputs": [ 252 | { 253 | "data": { 254 | "text/plain": [ 255 | "array([[1, 2],\n", 256 | " [3, 4],\n", 257 | " [5, 6]])" 258 | ] 259 | }, 260 | "execution_count": 14, 261 | "metadata": {}, 262 | "output_type": "execute_result" 263 | } 264 | ], 265 | "source": [ 266 | "np.array(python_list)" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 15, 272 | "id": "e56dbf39-4f4a-42ff-80fb-bb66001a4565", 273 | "metadata": {}, 274 | "outputs": [ 275 | { 276 | "data": { 277 | "text/plain": [ 278 | "array([ 0, 2, 4, 6, 8, 10])" 279 | ] 280 | }, 281 | "execution_count": 15, 282 | "metadata": {}, 283 | "output_type": "execute_result" 284 | } 285 | ], 286 | "source": [ 287 | "np.arange(0,11,2)" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 16, 293 | "id": "99989246-c773-4893-b9c6-1b4c9d27c2fe", 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" 300 | ] 301 | }, 302 | "execution_count": 16, 303 | "metadata": {}, 304 | "output_type": "execute_result" 305 | } 306 | ], 307 | "source": [ 308 | "np.zeros(10 , int)" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": 17, 314 | "id": "fca3b98f-0da3-4c82-ae40-300f132603e8", 315 | "metadata": {}, 316 | "outputs": [ 317 | { 318 | "data": { 319 | "text/plain": [ 320 | "array([[0, 0, 0],\n", 321 | " [0, 0, 0],\n", 322 | " [0, 0, 0]])" 323 | ] 324 | }, 325 | "execution_count": 17, 326 | "metadata": {}, 327 | "output_type": "execute_result" 328 | } 329 | ], 330 | "source": [ 331 | "np.zeros((3,3) , int)" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 18, 337 | "id": "e268c25d-4581-4441-9c4f-e956316e9bfc", 338 | "metadata": {}, 339 | "outputs": [ 340 | { 341 | "data": { 342 | "text/plain": [ 343 | "array([[[1., 1., 1., 1.],\n", 344 | " [1., 1., 1., 1.]],\n", 345 | "\n", 346 | " [[1., 1., 1., 1.],\n", 347 | " [1., 1., 1., 1.]],\n", 348 | "\n", 349 | " [[1., 1., 1., 1.],\n", 350 | " [1., 1., 1., 1.]],\n", 351 | "\n", 352 | " [[1., 1., 1., 1.],\n", 353 | " [1., 1., 1., 1.]],\n", 354 | "\n", 355 | " [[1., 1., 1., 1.],\n", 356 | " [1., 1., 1., 1.]]])" 357 | ] 358 | }, 359 | "execution_count": 18, 360 | "metadata": {}, 361 | "output_type": "execute_result" 362 | } 363 | ], 364 | "source": [ 365 | "np.ones((5,2,4))" 366 | ] 367 | }, 368 | { 369 | "cell_type": "code", 370 | "execution_count": 19, 371 | "id": "3b116d19-1650-46c5-8257-369c50b23cf2", 372 | "metadata": {}, 373 | "outputs": [ 374 | { 375 | "data": { 376 | "text/plain": [ 377 | "array([[1, 0, 0],\n", 378 | " [0, 1, 0],\n", 379 | " [0, 0, 1]])" 380 | ] 381 | }, 382 | "execution_count": 19, 383 | "metadata": {}, 384 | "output_type": "execute_result" 385 | } 386 | ], 387 | "source": [ 388 | "#diagonal matrices\n", 389 | "np.eye(3,dtype=int)" 390 | ] 391 | }, 392 | { 393 | "cell_type": "code", 394 | "execution_count": 20, 395 | "id": "14348881-36f8-4385-929a-767be93da987", 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "data": { 400 | "text/plain": [ 401 | "array([[0.23125289, 0.17657115, 0.73858594],\n", 402 | " [0.34483192, 0.40058807, 0.39550327],\n", 403 | " [0.37153114, 0.40700279, 0.77381529]])" 404 | ] 405 | }, 406 | "execution_count": 20, 407 | "metadata": {}, 408 | "output_type": "execute_result" 409 | } 410 | ], 411 | "source": [ 412 | "#between 0 and 1\n", 413 | "np.random.rand(3,3)" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 21, 419 | "id": "90199006-29a6-489c-ae4a-589652d6292d", 420 | "metadata": {}, 421 | "outputs": [ 422 | { 423 | "data": { 424 | "text/plain": [ 425 | "array([[ 0.43468629, 0.77698343, -0.26364651, 0.71574969],\n", 426 | " [-0.4778537 , -1.68156296, 0.85724506, 0.54020524]])" 427 | ] 428 | }, 429 | "execution_count": 21, 430 | "metadata": {}, 431 | "output_type": "execute_result" 432 | } 433 | ], 434 | "source": [ 435 | "# standard distribution contain variance number(negetive)\n", 436 | "np.random.randn(2,4)" 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "execution_count": 22, 442 | "id": "47667242-00bb-40d2-b3a7-2632f4190950", 443 | "metadata": {}, 444 | "outputs": [ 445 | { 446 | "data": { 447 | "text/plain": [ 448 | "array([[35, 47, 26, 4, 48],\n", 449 | " [42, 25, 16, 27, 10],\n", 450 | " [35, 27, 17, 31, 18]])" 451 | ] 452 | }, 453 | "execution_count": 22, 454 | "metadata": {}, 455 | "output_type": "execute_result" 456 | } 457 | ], 458 | "source": [ 459 | "np.random.randint(0,50,(3,5))" 460 | ] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": 23, 465 | "id": "e099b25c-c09a-403a-ad76-260fb4ebf380", 466 | "metadata": {}, 467 | "outputs": [ 468 | { 469 | "data": { 470 | "text/plain": [ 471 | "array([51, 92, 14, 71, 60, 20, 82, 86, 74, 74])" 472 | ] 473 | }, 474 | "execution_count": 23, 475 | "metadata": {}, 476 | "output_type": "execute_result" 477 | } 478 | ], 479 | "source": [ 480 | "# but this section doesn't change any compile and default value with np.random.seed()\n", 481 | "# especially in machine learning algorithms\n", 482 | "np.random.seed(42)\n", 483 | "np.random.randint(0,100,10)" 484 | ] 485 | }, 486 | { 487 | "cell_type": "code", 488 | "execution_count": 24, 489 | "id": "33039e74-390f-4f16-a61e-e081aac46ead", 490 | "metadata": {}, 491 | "outputs": [ 492 | { 493 | "data": { 494 | "text/plain": [ 495 | "array([87, 99, 23, 2, 21, 52, 1, 87, 29, 37])" 496 | ] 497 | }, 498 | "execution_count": 24, 499 | "metadata": {}, 500 | "output_type": "execute_result" 501 | } 502 | ], 503 | "source": [ 504 | "# in any compile reproduce new value\n", 505 | "np.random.randint(0,100,10)" 506 | ] 507 | }, 508 | { 509 | "cell_type": "code", 510 | "execution_count": 25, 511 | "id": "1b0e1455-9b5b-4b9d-b813-0395cbdac563", 512 | "metadata": {}, 513 | "outputs": [], 514 | "source": [ 515 | "ex_arr = np.random.randint(0,100,10)" 516 | ] 517 | }, 518 | { 519 | "cell_type": "code", 520 | "execution_count": 26, 521 | "id": "12543062-2799-4d00-8280-3dbb20dc2345", 522 | "metadata": {}, 523 | "outputs": [ 524 | { 525 | "data": { 526 | "text/plain": [ 527 | "numpy.ndarray" 528 | ] 529 | }, 530 | "execution_count": 26, 531 | "metadata": {}, 532 | "output_type": "execute_result" 533 | } 534 | ], 535 | "source": [ 536 | "type(ex_arr)" 537 | ] 538 | }, 539 | { 540 | "cell_type": "code", 541 | "execution_count": 27, 542 | "id": "29f55460-b5f3-4b10-af0a-96086e975a57", 543 | "metadata": {}, 544 | "outputs": [ 545 | { 546 | "data": { 547 | "text/plain": [ 548 | "dtype('int32')" 549 | ] 550 | }, 551 | "execution_count": 27, 552 | "metadata": {}, 553 | "output_type": "execute_result" 554 | } 555 | ], 556 | "source": [ 557 | "ex_arr.dtype" 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": 28, 563 | "id": "4e4f3fa4-059e-43b7-ac97-fa60aa53ba6f", 564 | "metadata": {}, 565 | "outputs": [ 566 | { 567 | "data": { 568 | "text/plain": [ 569 | "(10,)" 570 | ] 571 | }, 572 | "execution_count": 28, 573 | "metadata": {}, 574 | "output_type": "execute_result" 575 | } 576 | ], 577 | "source": [ 578 | "ex_arr.shape" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": 29, 584 | "id": "7f1ffe8c-23a0-4d38-82d8-df14d8450f1a", 585 | "metadata": {}, 586 | "outputs": [ 587 | { 588 | "data": { 589 | "text/plain": [ 590 | "array([[ 1, 63, 59, 20, 32],\n", 591 | " [75, 57, 21, 88, 48]])" 592 | ] 593 | }, 594 | "execution_count": 29, 595 | "metadata": {}, 596 | "output_type": "execute_result" 597 | } 598 | ], 599 | "source": [ 600 | "ex_arr.reshape(2,5)" 601 | ] 602 | }, 603 | { 604 | "cell_type": "code", 605 | "execution_count": 30, 606 | "id": "adc377f2-3d76-430c-bb28-f30e99e6494f", 607 | "metadata": {}, 608 | "outputs": [ 609 | { 610 | "data": { 611 | "text/plain": [ 612 | "88" 613 | ] 614 | }, 615 | "execution_count": 30, 616 | "metadata": {}, 617 | "output_type": "execute_result" 618 | } 619 | ], 620 | "source": [ 621 | "ex_arr.max()" 622 | ] 623 | }, 624 | { 625 | "cell_type": "code", 626 | "execution_count": 31, 627 | "id": "63c679b1-d835-4235-906d-1394a39c2df5", 628 | "metadata": {}, 629 | "outputs": [ 630 | { 631 | "data": { 632 | "text/plain": [ 633 | "8" 634 | ] 635 | }, 636 | "execution_count": 31, 637 | "metadata": {}, 638 | "output_type": "execute_result" 639 | } 640 | ], 641 | "source": [ 642 | "ex_arr.argmax()" 643 | ] 644 | }, 645 | { 646 | "cell_type": "code", 647 | "execution_count": 32, 648 | "id": "01281f3f-5c31-400e-bd6d-8ef06210b964", 649 | "metadata": {}, 650 | "outputs": [], 651 | "source": [ 652 | "arr_2d = np.array([[1,2,3],[4,5,6],[7,8,9]])" 653 | ] 654 | }, 655 | { 656 | "cell_type": "code", 657 | "execution_count": 33, 658 | "id": "4acc3789-fcca-4b5c-85d8-e33468c908c1", 659 | "metadata": {}, 660 | "outputs": [ 661 | { 662 | "data": { 663 | "text/plain": [ 664 | "(3, 3)" 665 | ] 666 | }, 667 | "execution_count": 33, 668 | "metadata": {}, 669 | "output_type": "execute_result" 670 | } 671 | ], 672 | "source": [ 673 | "arr_2d.shape" 674 | ] 675 | }, 676 | { 677 | "cell_type": "code", 678 | "execution_count": 34, 679 | "id": "fb9f7640-d7f7-4706-bde2-c336c792ee5f", 680 | "metadata": {}, 681 | "outputs": [ 682 | { 683 | "data": { 684 | "text/plain": [ 685 | "array([[4, 5, 6]])" 686 | ] 687 | }, 688 | "execution_count": 34, 689 | "metadata": {}, 690 | "output_type": "execute_result" 691 | } 692 | ], 693 | "source": [ 694 | "arr_2d[1:2]" 695 | ] 696 | }, 697 | { 698 | "cell_type": "code", 699 | "execution_count": 35, 700 | "id": "7006585b-a31e-4bec-8504-672431ada2aa", 701 | "metadata": {}, 702 | "outputs": [], 703 | "source": [ 704 | "arr_copy = arr_2d.copy()" 705 | ] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": 36, 710 | "id": "50733767-33f8-4665-8340-3fef829da337", 711 | "metadata": {}, 712 | "outputs": [ 713 | { 714 | "data": { 715 | "text/plain": [ 716 | "array([[4, 5],\n", 717 | " [7, 8]])" 718 | ] 719 | }, 720 | "execution_count": 36, 721 | "metadata": {}, 722 | "output_type": "execute_result" 723 | } 724 | ], 725 | "source": [ 726 | "arr_copy[1:,:2]" 727 | ] 728 | }, 729 | { 730 | "cell_type": "code", 731 | "execution_count": 37, 732 | "id": "df1c8e69-8a8f-4c4a-9335-4eac25fe8bde", 733 | "metadata": {}, 734 | "outputs": [], 735 | "source": [ 736 | "arr_bool = arr_copy > 4" 737 | ] 738 | }, 739 | { 740 | "cell_type": "code", 741 | "execution_count": 38, 742 | "id": "c9efd0d5-d984-4f91-b2d2-a46049540e07", 743 | "metadata": {}, 744 | "outputs": [ 745 | { 746 | "data": { 747 | "text/plain": [ 748 | "array([5, 6, 7, 8, 9])" 749 | ] 750 | }, 751 | "execution_count": 38, 752 | "metadata": {}, 753 | "output_type": "execute_result" 754 | } 755 | ], 756 | "source": [ 757 | "arr_copy[arr_bool]" 758 | ] 759 | }, 760 | { 761 | "cell_type": "code", 762 | "execution_count": 39, 763 | "id": "4c753625-9fed-4846-a8db-fe8ab190eaae", 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "45" 770 | ] 771 | }, 772 | "execution_count": 39, 773 | "metadata": {}, 774 | "output_type": "execute_result" 775 | } 776 | ], 777 | "source": [ 778 | "arr_copy.sum()" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": 40, 784 | "id": "eb4be50c-2fe0-4a58-81c0-54156e9aeb1d", 785 | "metadata": {}, 786 | "outputs": [ 787 | { 788 | "data": { 789 | "text/plain": [ 790 | "5.0" 791 | ] 792 | }, 793 | "execution_count": 40, 794 | "metadata": {}, 795 | "output_type": "execute_result" 796 | } 797 | ], 798 | "source": [ 799 | "arr_copy.mean()" 800 | ] 801 | }, 802 | { 803 | "cell_type": "code", 804 | "execution_count": 41, 805 | "id": "279c5632-78aa-481c-b26e-54d4b1679a2c", 806 | "metadata": {}, 807 | "outputs": [], 808 | "source": [ 809 | "arr_5_5 = np.arange(25).reshape(5,5)" 810 | ] 811 | }, 812 | { 813 | "cell_type": "code", 814 | "execution_count": 42, 815 | "id": "36d0b85e-30cf-471a-a032-cb0a2ebc5c66", 816 | "metadata": {}, 817 | "outputs": [ 818 | { 819 | "data": { 820 | "text/plain": [ 821 | "array([[ 0, 1, 2, 3, 4],\n", 822 | " [ 5, 6, 7, 8, 9],\n", 823 | " [10, 11, 12, 13, 14],\n", 824 | " [15, 16, 17, 18, 19],\n", 825 | " [20, 21, 22, 23, 24]])" 826 | ] 827 | }, 828 | "execution_count": 42, 829 | "metadata": {}, 830 | "output_type": "execute_result" 831 | } 832 | ], 833 | "source": [ 834 | "arr_5_5" 835 | ] 836 | }, 837 | { 838 | "cell_type": "code", 839 | "execution_count": 43, 840 | "id": "0ef86885-5728-456b-a970-8d5cb2531147", 841 | "metadata": {}, 842 | "outputs": [ 843 | { 844 | "data": { 845 | "text/plain": [ 846 | "array([50, 55, 60, 65, 70])" 847 | ] 848 | }, 849 | "execution_count": 43, 850 | "metadata": {}, 851 | "output_type": "execute_result" 852 | } 853 | ], 854 | "source": [ 855 | "# column by column \n", 856 | "arr_5_5.sum(axis = 0)" 857 | ] 858 | }, 859 | { 860 | "cell_type": "code", 861 | "execution_count": 44, 862 | "id": "c0f647f1-4f5d-43e0-b67d-4cf99c9ede17", 863 | "metadata": {}, 864 | "outputs": [ 865 | { 866 | "data": { 867 | "text/plain": [ 868 | "array([ 10, 35, 60, 85, 110])" 869 | ] 870 | }, 871 | "execution_count": 44, 872 | "metadata": {}, 873 | "output_type": "execute_result" 874 | } 875 | ], 876 | "source": [ 877 | "# row by row\n", 878 | "arr_5_5.sum(axis = 1)" 879 | ] 880 | }, 881 | { 882 | "cell_type": "code", 883 | "execution_count": 45, 884 | "id": "057bff41-68bc-4f28-876f-f724e251ade3", 885 | "metadata": {}, 886 | "outputs": [ 887 | { 888 | "data": { 889 | "text/plain": [ 890 | "array([ 2., 7., 12., 17., 22.])" 891 | ] 892 | }, 893 | "execution_count": 45, 894 | "metadata": {}, 895 | "output_type": "execute_result" 896 | } 897 | ], 898 | "source": [ 899 | "# average of first row \n", 900 | "arr_5_5.mean(axis=1)" 901 | ] 902 | }, 903 | { 904 | "cell_type": "code", 905 | "execution_count": 48, 906 | "id": "bbac4955-4e1b-47ff-9419-40f7804ef064", 907 | "metadata": {}, 908 | "outputs": [ 909 | { 910 | "data": { 911 | "text/plain": [ 912 | "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" 913 | ] 914 | }, 915 | "execution_count": 48, 916 | "metadata": {}, 917 | "output_type": "execute_result" 918 | } 919 | ], 920 | "source": [ 921 | "# use in neural networks to flat the array \n", 922 | "arr_2d.flatten()" 923 | ] 924 | }, 925 | { 926 | "cell_type": "code", 927 | "execution_count": 51, 928 | "id": "6206f7a9-d696-4ae6-bea5-6ec11165ee28", 929 | "metadata": {}, 930 | "outputs": [ 931 | { 932 | "data": { 933 | "text/plain": [ 934 | "array([[1, 2, 3],\n", 935 | " [4, 5, 6],\n", 936 | " [7, 8, 9]])" 937 | ] 938 | }, 939 | "execution_count": 51, 940 | "metadata": {}, 941 | "output_type": "execute_result" 942 | } 943 | ], 944 | "source": [ 945 | "arr_2d" 946 | ] 947 | }, 948 | { 949 | "cell_type": "code", 950 | "execution_count": 53, 951 | "id": "9e912888-02c1-4549-b84f-ee4c99a55225", 952 | "metadata": {}, 953 | "outputs": [ 954 | { 955 | "data": { 956 | "text/plain": [ 957 | "array([7, 8, 9])" 958 | ] 959 | }, 960 | "execution_count": 53, 961 | "metadata": {}, 962 | "output_type": "execute_result" 963 | } 964 | ], 965 | "source": [ 966 | "arr_2d.max(axis=0)" 967 | ] 968 | }, 969 | { 970 | "cell_type": "code", 971 | "execution_count": 55, 972 | "id": "9cca3470-c8fa-4719-9897-2b46e198fb47", 973 | "metadata": {}, 974 | "outputs": [ 975 | { 976 | "data": { 977 | "text/plain": [ 978 | "array([1, 4, 7])" 979 | ] 980 | }, 981 | "execution_count": 55, 982 | "metadata": {}, 983 | "output_type": "execute_result" 984 | } 985 | ], 986 | "source": [ 987 | "arr_2d.min(axis=1)" 988 | ] 989 | }, 990 | { 991 | "cell_type": "code", 992 | "execution_count": 58, 993 | "id": "5448699e-68fd-4478-b023-d938f8c99625", 994 | "metadata": {}, 995 | "outputs": [ 996 | { 997 | "data": { 998 | "text/plain": [ 999 | "array([[0, 0, 0],\n", 1000 | " [0, 1, 1],\n", 1001 | " [1, 1, 1]])" 1002 | ] 1003 | }, 1004 | "execution_count": 58, 1005 | "metadata": {}, 1006 | "output_type": "execute_result" 1007 | } 1008 | ], 1009 | "source": [ 1010 | "# condition in array use np.where \n", 1011 | "np.where(arr_2d >= 5 , 1 , 0)" 1012 | ] 1013 | }, 1014 | { 1015 | "cell_type": "code", 1016 | "execution_count": 73, 1017 | "id": "1d6157c6-e908-4dba-8c97-10ec4e7edb53", 1018 | "metadata": {}, 1019 | "outputs": [], 1020 | "source": [ 1021 | "np.random.shuffle(arr_5_5)\n", 1022 | "np.random.seed(55)" 1023 | ] 1024 | }, 1025 | { 1026 | "cell_type": "code", 1027 | "execution_count": 74, 1028 | "id": "e483a583-ba09-4509-8766-a1dca3c61f46", 1029 | "metadata": {}, 1030 | "outputs": [ 1031 | { 1032 | "data": { 1033 | "text/plain": [ 1034 | "array([[20, 21, 22, 23, 24],\n", 1035 | " [ 5, 6, 7, 8, 9],\n", 1036 | " [15, 16, 17, 18, 19],\n", 1037 | " [10, 11, 12, 13, 14],\n", 1038 | " [ 0, 1, 2, 3, 4]])" 1039 | ] 1040 | }, 1041 | "execution_count": 74, 1042 | "metadata": {}, 1043 | "output_type": "execute_result" 1044 | } 1045 | ], 1046 | "source": [ 1047 | "arr_5_5" 1048 | ] 1049 | }, 1050 | { 1051 | "cell_type": "code", 1052 | "execution_count": 83, 1053 | "id": "80241cb5-638a-4f07-be24-eb37d5ea4277", 1054 | "metadata": {}, 1055 | "outputs": [ 1056 | { 1057 | "data": { 1058 | "text/plain": [ 1059 | "array([8, 1, 5, 0, 7, 2, 9, 4, 3, 6])" 1060 | ] 1061 | }, 1062 | "execution_count": 83, 1063 | "metadata": {}, 1064 | "output_type": "execute_result" 1065 | } 1066 | ], 1067 | "source": [ 1068 | "np.random.seed(42)\n", 1069 | "np.random.permutation(10)" 1070 | ] 1071 | }, 1072 | { 1073 | "cell_type": "code", 1074 | "execution_count": 85, 1075 | "id": "f5024991-565c-4aef-a7ad-903bc6825b84", 1076 | "metadata": {}, 1077 | "outputs": [ 1078 | { 1079 | "data": { 1080 | "text/plain": [ 1081 | "array([[20, 21, 22, 23, 24],\n", 1082 | " [ 5, 6, 7, 8, 9],\n", 1083 | " [15, 16, 17, 18, 19],\n", 1084 | " [10, 11, 12, 13, 14],\n", 1085 | " [ 0, 1, 2, 3, 4]])" 1086 | ] 1087 | }, 1088 | "execution_count": 85, 1089 | "metadata": {}, 1090 | "output_type": "execute_result" 1091 | } 1092 | ], 1093 | "source": [ 1094 | "np.random.permutation(arr_5_5)" 1095 | ] 1096 | }, 1097 | { 1098 | "cell_type": "code", 1099 | "execution_count": 104, 1100 | "id": "ac266379-2e8e-42d7-a5ce-492bc9da32f9", 1101 | "metadata": {}, 1102 | "outputs": [ 1103 | { 1104 | "data": { 1105 | "text/plain": [ 1106 | "[6, 7, 5]" 1107 | ] 1108 | }, 1109 | "execution_count": 104, 1110 | "metadata": {}, 1111 | "output_type": "execute_result" 1112 | } 1113 | ], 1114 | "source": [ 1115 | "from random import sample \n", 1116 | "sample([2,5,6,7,8,9], 3)\n" 1117 | ] 1118 | } 1119 | ], 1120 | "metadata": { 1121 | "kernelspec": { 1122 | "display_name": "Python 3 (ipykernel)", 1123 | "language": "python", 1124 | "name": "python3" 1125 | }, 1126 | "language_info": { 1127 | "codemirror_mode": { 1128 | "name": "ipython", 1129 | "version": 3 1130 | }, 1131 | "file_extension": ".py", 1132 | "mimetype": "text/x-python", 1133 | "name": "python", 1134 | "nbconvert_exporter": "python", 1135 | "pygments_lexer": "ipython3", 1136 | "version": "3.8.10" 1137 | } 1138 | }, 1139 | "nbformat": 4, 1140 | "nbformat_minor": 5 1141 | } 1142 | -------------------------------------------------------------------------------- /Exercise Notebooks/Training_a_Convolutional_Layer_With_Recognition_Model_Using_Pytorch_on_the_MNIST_Dataset.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 9, 6 | "id": "12ffd64b-2022-4af4-98c8-1ef885cfb370", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "# add requierment library to define & train model \n", 11 | "import torch\n", 12 | "import torch.nn as nn\n", 13 | "import torch.nn.functional as F\n", 14 | "from torch.utils.data import DataLoader\n", 15 | "from torchvision import datasets, transforms\n", 16 | "from torchvision.utils import make_grid\n", 17 | "\n", 18 | "import numpy as np\n", 19 | "import pandas as pd\n", 20 | "from sklearn.metrics import confusion_matrix\n", 21 | "import matplotlib.pyplot as plt" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 10, 27 | "id": "69bcc818-49f6-4d5a-85b5-096e39de7df7", 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "# download data train & test MNIST dataset from pytorchvision library \n", 32 | "transform = transforms.ToTensor()\n", 33 | "\n", 34 | "train_data = datasets.MNIST(root='/Data/MNIST_From_Download_Pytorchvision/train', train=True, download=True, transform=transform)\n", 35 | "test_data = datasets.MNIST(root='/Data/MNIST_From_Download_Pytorchvision/test', train=False, download=True, transform=transform)" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 12, 41 | "id": "65553a84-02ae-4961-982c-847bcf2ccc8f", 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/plain": [ 47 | "Dataset MNIST\n", 48 | " Number of datapoints: 60000\n", 49 | " Root location: /Data/MNIST_From_Download_Pytorchvision/train\n", 50 | " Split: Train\n", 51 | " StandardTransform\n", 52 | "Transform: ToTensor()" 53 | ] 54 | }, 55 | "execution_count": 12, 56 | "metadata": {}, 57 | "output_type": "execute_result" 58 | } 59 | ], 60 | "source": [ 61 | "# check amount of data train dataset\n", 62 | "train_data" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 13, 68 | "id": "9fb190dd-568f-47f9-8e54-c9115f2ce030", 69 | "metadata": {}, 70 | "outputs": [ 71 | { 72 | "data": { 73 | "text/plain": [ 74 | "Dataset MNIST\n", 75 | " Number of datapoints: 10000\n", 76 | " Root location: /Data/MNIST_From_Download_Pytorchvision/test\n", 77 | " Split: Test\n", 78 | " StandardTransform\n", 79 | "Transform: ToTensor()" 80 | ] 81 | }, 82 | "execution_count": 13, 83 | "metadata": {}, 84 | "output_type": "execute_result" 85 | } 86 | ], 87 | "source": [ 88 | "# check amount of data test dataset\n", 89 | "test_data" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 14, 95 | "id": "f1e648cb-a79c-42fa-ae74-5a1bf472ecd2", 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "# define train & test loader to load data to use it into model \n", 100 | "train_loader = DataLoader(train_data, batch_size=10, shuffle=True)\n", 101 | "test_loader = DataLoader(test_data, batch_size=10, shuffle=False)" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "id": "d5bf3328-2272-4ca1-9805-6664a04f7533", 108 | "metadata": {}, 109 | "outputs": [], 110 | "source": [] 111 | } 112 | ], 113 | "metadata": { 114 | "kernelspec": { 115 | "display_name": "Python 3 (ipykernel)", 116 | "language": "python", 117 | "name": "python3" 118 | }, 119 | "language_info": { 120 | "codemirror_mode": { 121 | "name": "ipython", 122 | "version": 3 123 | }, 124 | "file_extension": ".py", 125 | "mimetype": "text/x-python", 126 | "name": "python", 127 | "nbconvert_exporter": "python", 128 | "pygments_lexer": "ipython3", 129 | "version": "3.8.10" 130 | } 131 | }, 132 | "nbformat": 4, 133 | "nbformat_minor": 5 134 | } 135 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Yasin Rezvani 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Hands_On_Machine_Learning_and_Deep_Learning_with_Python 2 | - **Supervisor:** [Dr. Mansoor Fateh](https://scholar.google.com/citations?user=ZHezeMIAAAAJ&hl=en)
3 | - **Organization:** [Computer Vision Lab at Shahrood University of Technology (CVLab SHUT)](https://github.com/CVLab-SHUT)
4 | 5 | This project was conducted under the supervision of Dr. Mansoor Fateh at CVLab SHUT, focusing on deep learning and computer vision research. The work utilized Python along with key libraries including NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualization, as well as Keras, TensorFlow, and PyTorch for implementing and training deep learning models on standard datasets (MNIST and CIFAR-10). The research aimed to develop practical skills in building and optimizing neural networks while adhering to academic methodologies. 6 | # Demo :tada: 7 | ## :fireworks: Loading_The_MNIST_Dataset :fireworks: 8 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/416b0619-daa0-41d1-9a76-a0e3fa4b4e56 9 | 10 | ## :maple_leaf: Playing_With_Numpy_Library :maple_leaf: 11 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/2c576aca-3831-4607-858e-dc6abdeae14a 12 | 13 | ## :gem: NumPy_Exercises_From_Udemy_Course :gem: 14 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/3d409ede-ac5e-4c7e-ac47-2f898e2e3b82 15 | 16 | ## :v: Playing_With_Pandas_Library :v: 17 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/b519cef7-d962-4f22-9ed0-6a6da0ef7762 18 | 19 | ## :muscle: Pandas_Exercises_From_Udemy_Course :muscle: 20 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/92a618ca-9fd8-404e-8de9-fecf9962c2bb 21 | 22 | ## :sparkling_heart: Playing_With_Scipy_Library :sparkling_heart: 23 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/454d1b23-94c4-4412-9c08-367e8a2b37de 24 | 25 | ## :airplane: Playing_With_Matplotlib_Library :airplane: 26 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/095c7f36-3ad1-4bce-9114-b1a5ff51ebc6 27 | 28 | ## :rocket: Playing_With_Seaborn_Library :rocket: 29 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/0c66a19f-b0ee-40f5-affe-cc24fcd9d3d6 30 | 31 | ## :christmas_tree: Training_a_Digit_Recognition_Model_Using_Keras_on_the_MNIST_Dataset :christmas_tree: 32 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/cb97439a-66ff-44d0-92a4-f39808b4f353 33 | 34 | ## :chart_with_upwards_trend: Training_a_Recognition_Model_Using_Keras_on_the_CIFAR_10_Dataset :chart_with_upwards_trend: 35 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/a53bce7d-0439-43a4-b378-20d0c399b17c 36 | 37 | ## :spades: Training_a_Convolution_Dense_Layer_With_Recognition_Model_Using_Keras_on_the_CIFAR_10_Dataset :spades: 38 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/8afe3d84-148c-4f04-a8e0-09e67bc4703f 39 | 40 | ## :statue_of_liberty: How_to_Define_Functional_And_Sequential_Keras_Models :statue_of_liberty: 41 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/a5dff226-5877-4e00-8a64-e12aeb9e4b60 42 | 43 | ## :bomb: Training_a_Digit_Recognition_Model_Using_Pytorch_Library_on_the_MNIST_Dataset :bomb: 44 | https://github.com/YasinRezvani/Python_Learning_Practice_Notebooks/assets/77124662/9520b1bf-1245-4d33-92b2-fcde703463bb 45 | 46 | 47 | 48 | 49 | --------------------------------------------------------------------------------