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1 | # A New Programming Paradigm
2 |
3 | ## An Overview
4 | * One of the best tools we can use to implement deep learning and machine learning algorithms is TensorFlow.
5 | * Programming frameworks like TensorFlow, PyTorchm caffe and many others can help us in learning algorithms, moreover they can save a lot of time.
6 | * Deep learning and machine learning skills are becoming ever more important and opening up whole new scenarios.
7 | * Even though the whole world sees the promise and the hope of these machine learning AI capabilities changing so many things, the world just doesn't have enough AI developers today.
8 |
9 | ## Notebooks for This Course
10 | Ungraded labs and assignment for each week can be found in this [Github repository](https://github.com/https-deeplearning-ai/tensorflow-1-public).
11 | How to run the notebooks locally:
12 | ```
13 | git clone https://github.com/https-deeplearning-ai/tensorflow-1-public
14 | git pull
15 | ```
16 | Packages:
17 | ```
18 | tensorflow==2.7.0
19 | scikit-learn==1.0.1
20 | pandas==1.1.5
21 | matplotlib==3.2.2
22 | seaborn==0.11.2
23 | ```
24 |
25 | ## A Primer in Machine Learning
26 |
27 |
28 |
Image 1. Traditional Programming
30 | 31 |
32 | 
33 |
Image 2. Activity Recognition using Traditional Programming
35 | 36 |
37 | 
38 |
Image 3. New Programming
40 | 41 | * Rules are express in a programming language and data can come from a variety of sources from local variables or the way up to databases 42 | * Machine learning is really similar but we only flipping the axes 43 | * Neural network is the workhorse of doing this type of pattern recognition 44 | 45 | ## The ‘Hello World’ of neural networks 46 | Machine learning is all about a computer learning the patterns that distinguish things. The simplest possible neural network is one that has only one neuron in it, we can see it the code below. 47 | 48 | ```python 49 | # # In keras, we use Dense to define a layer of connected neurons 50 | model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])]) 51 | model.compile(optimizer='sgd', loss='mean_squared_error') 52 | 53 | xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float) 54 | ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype-float) 55 | 56 | # the training takes place in the fit command 57 | # epoch = 500, means that it will go through the training loop 500 times 58 | model.fit(xs, ys, epochs=500) 59 | 60 | print(model.predict([10.0])) 61 | ``` 62 | 63 | There are two functions, **loss** and **optimizer**. 64 | * Loss function measure how good or how bad its guess was. Loss function is mean squarred error. 65 | * Optimizer function figures out the next guess, how good or how badly the guess was done using the data from the loss function. Each guess should be better than the one before. Optimizer is SGD (Stochastic Gradient Descent) 66 | 67 | In conclusion, the steps to figure out the patterns are: 68 | 1. Make a guess 69 | 2. Measure how good or how bad the guesses with the loss function 70 | 3. Use the optimizer and the data to make another guess and repeat this 71 | 72 | If the result is far from expectation or what you guess before, there are 2 reasons for it. 73 | 1. Training data is too small 74 | 2. When using neural networks, they deal in probability as they try to figure out the answers for everything. 75 | 76 | ## From Rules to Data 77 | * Traditional paradigm of expressing rules in a coding language may not always work to solve a problem. Computer vision are very difficult to solve with rules-based programming. 78 | * We can feed a computer with enough data that we describe / label as what we want it to recognize. Example: fitting numbers to a line. 79 | 80 | ## Get started with Google Colaboratory (Coding TensorFlow) 81 | 82 | * One useful notebook collection: [Seedbank Project](research.google.com/seedbank) 83 | * Start working with Google Colab: [Welcome to Colaboratory](colab.research.google.com) 84 | * An easy way to learn and use Tensorflow in Colab: [Colab - Tensorflow](https://medium.com/tensorflow/colab-an-easy-way-to-learn-and-use-tensorflow-d74d1686e309) 85 | * Week 1 - Workbook: [Hello World of Deep Learning with Neural Networks](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C1/W1/ungraded_lab/C1_W1_Lab_1_hello_world_nn.ipynb) 86 | 87 | ## Week 1 Grading Assignments 88 | 89 | * Quiz 1: [week1-quiz](Graded%20Assignments/week1-quiz.md) 90 | * Programming assignment: [notebook](Graded%20Assignments/C1W1_Assignment.ipynb) 91 | 92 | ## Resources 93 | * AI for everyone: [ai-for-everyone-course](https://www.deeplearning.ai/program/ai-for-everyone/) 94 | * Tensorflow: [documentation](https://www.tensorflow.org/) and [youtube](https://www.youtube.com/tensorflow) 95 | * Play with neural networks: [neural-network](http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=0&networkShape=4,2&seed=0.83151&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false) -------------------------------------------------------------------------------- /1. 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Introduction to TensorFlow/week 2/Graded Assignment/C1W2_Assignment.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "_2s0EJ5Fy4u2" 7 | }, 8 | "source": [ 9 | "# Week 2: Implementing Callbacks in TensorFlow using the MNIST Dataset\n", 10 | "\n", 11 | "In the course you learned how to do classification using Fashion MNIST, a data set containing items of clothing. There's another, similar dataset called MNIST which has items of handwriting -- the digits 0 through 9.\n", 12 | "\n", 13 | "Write an MNIST classifier that trains to 99% accuracy or above, and does it without a fixed number of epochs -- i.e. you should stop training once you reach that level of accuracy. In the lecture you saw how this was done for the loss but here you will be using accuracy instead.\n", 14 | "\n", 15 | "Some notes:\n", 16 | "1. Given the architecture of the net, it should succeed in less than 10 epochs.\n", 17 | "2. When it reaches 99% or greater it should print out the string \"Reached 99% accuracy so cancelling training!\" and stop training.\n", 18 | "3. If you add any additional variables, make sure you use the same names as the ones used in the class. This is important for the function signatures (the parameters and names) of the callbacks." 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 1, 24 | "metadata": { 25 | "id": "djVOgMHty4u3" 26 | }, 27 | "outputs": [], 28 | "source": [ 29 | "import os\n", 30 | "import tensorflow as tf\n", 31 | "from tensorflow import keras" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "Begin by loading the data. A couple of things to notice:\n", 39 | "\n", 40 | "- The file `mnist.npz` is already included in the current workspace under the `data` directory. By default the `load_data` from Keras accepts a path relative to `~/.keras/datasets` but in this case it is stored somewhere else, as a result of this, you need to specify the full path.\n", 41 | "\n", 42 | "- `load_data` returns the train and test sets in the form of the tuples `(x_train, y_train), (x_test, y_test)` but in this exercise you will be needing only the train set so you can ignore the second tuple." 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 2, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "# Load the data\n", 52 | "\n", 53 | "# Get current working directory\n", 54 | "current_dir = os.getcwd()\n", 55 | "\n", 56 | "# Append data/mnist.npz to the previous path to get the full path\n", 57 | "data_path = os.path.join(current_dir, \"data/mnist.npz\")\n", 58 | "\n", 59 | "# Discard test set\n", 60 | "(x_train, y_train), _ = tf.keras.datasets.mnist.load_data(path=data_path)\n", 61 | " \n", 62 | "# Normalize pixel values\n", 63 | "x_train = x_train / 255.0" 64 | ] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "metadata": {}, 69 | "source": [ 70 | "Now take a look at the shape of the training data:" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 3, 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "name": "stdout", 80 | "output_type": "stream", 81 | "text": [ 82 | "There are 60000 examples with shape (28, 28)\n" 83 | ] 84 | } 85 | ], 86 | "source": [ 87 | "data_shape = x_train.shape\n", 88 | "\n", 89 | "print(f\"There are {data_shape[0]} examples with shape ({data_shape[1]}, {data_shape[2]})\")" 90 | ] 91 | }, 92 | { 93 | "cell_type": "markdown", 94 | "metadata": {}, 95 | "source": [ 96 | "Now it is time to create your own custom callback. For this complete the `myCallback` class and the `on_epoch_end` method in the cell below. If you need some guidance on how to proceed, check out this [link](https://www.tensorflow.org/guide/keras/custom_callback)." 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 10, 102 | "metadata": {}, 103 | "outputs": [], 104 | "source": [ 105 | "# GRADED CLASS: myCallback\n", 106 | "### START CODE HERE\n", 107 | "\n", 108 | "# Remember to inherit from the correct class\n", 109 | "class myCallback(tf.keras.callbacks.Callback):\n", 110 | " # Define the correct function signature for on_epoch_end\n", 111 | " def on_epoch_end(self, epoch, logs={}):\n", 112 | " if logs.get('accuracy') is not None and logs.get('accuracy') > 0.99:\n", 113 | " print(\"\\nReached 99% accuracy so cancelling training!\") \n", 114 | " \n", 115 | " # Stop training once the above condition is met\n", 116 | " self.model.stop_training = True\n", 117 | "\n", 118 | "### END CODE HERE\n" 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": {}, 124 | "source": [ 125 | "Now that you have defined your callback it is time to complete the `train_mnist` function below:" 126 | ] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": 13, 131 | "metadata": { 132 | "id": "rEHcB3kqyHZ6" 133 | }, 134 | "outputs": [], 135 | "source": [ 136 | "# GRADED FUNCTION: train_mnist\n", 137 | "def train_mnist(x_train, y_train):\n", 138 | "\n", 139 | " ### START CODE HERE\n", 140 | " \n", 141 | " # Instantiate the callback class\n", 142 | " callbacks = myCallback()\n", 143 | " \n", 144 | " # Define the model, it should have 3 layers:\n", 145 | " # - A Flatten layer that receives inputs with the same shape as the images\n", 146 | " # - A Dense layer with 512 units and ReLU activation function\n", 147 | " # - A Dense layer with 10 units and softmax activation function\n", 148 | " model = tf.keras.models.Sequential([ \n", 149 | " tf.keras.layers.Flatten(input_shape=(28,28)),\n", 150 | " tf.keras.layers.Dense(512, activation=tf.nn.relu),\n", 151 | " tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n", 152 | " ]) \n", 153 | "\n", 154 | " # Compile the model\n", 155 | " model.compile(optimizer='adam', \n", 156 | " loss='sparse_categorical_crossentropy', \n", 157 | " metrics=['accuracy']) \n", 158 | " \n", 159 | " # Fit the model for 10 epochs adding the callbacks\n", 160 | " # and save the training history\n", 161 | " history = model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])\n", 162 | "\n", 163 | " ### END CODE HERE\n", 164 | "\n", 165 | " return history" 166 | ] 167 | }, 168 | { 169 | "cell_type": "markdown", 170 | "metadata": {}, 171 | "source": [ 172 | "Call the `train_mnist` passing in the appropiate parameters to get the training history:" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 14, 178 | "metadata": { 179 | "id": "sFgpwbGly4u4" 180 | }, 181 | "outputs": [ 182 | { 183 | "name": "stdout", 184 | "output_type": "stream", 185 | "text": [ 186 | "Epoch 1/10\n", 187 | "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1990 - accuracy: 0.9420\n", 188 | "Epoch 2/10\n", 189 | "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0820 - accuracy: 0.9749\n", 190 | "Epoch 3/10\n", 191 | "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0523 - accuracy: 0.9837\n", 192 | "Epoch 4/10\n", 193 | "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0377 - accuracy: 0.9873\n", 194 | "Epoch 5/10\n", 195 | "1870/1875 [============================>.] - ETA: 0s - loss: 0.0269 - accuracy: 0.9912\n", 196 | "Reached 99% accuracy so cancelling training!\n", 197 | "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0269 - accuracy: 0.9912\n" 198 | ] 199 | } 200 | ], 201 | "source": [ 202 | "hist = train_mnist(x_train, y_train)" 203 | ] 204 | }, 205 | { 206 | "cell_type": "markdown", 207 | "metadata": {}, 208 | "source": [ 209 | "If you see the message `Reached 99% accuracy so cancelling training!` printed out after less than 10 epochs it means your callback worked as expected. " 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "metadata": {}, 215 | "source": [ 216 | "**Congratulations on finishing this week's assignment!**\n", 217 | "\n", 218 | "You have successfully implemented a callback that gives you more control over the training loop for your model. Nice job!\n", 219 | "\n", 220 | "**Keep it up!**" 221 | ] 222 | } 223 | ], 224 | "metadata": { 225 | "jupytext": { 226 | "main_language": "python" 227 | }, 228 | "kernelspec": { 229 | "display_name": "Python 3", 230 | "language": "python", 231 | "name": "python3" 232 | }, 233 | "language_info": { 234 | "codemirror_mode": { 235 | "name": "ipython", 236 | "version": 3 237 | }, 238 | "file_extension": ".py", 239 | "mimetype": "text/x-python", 240 | "name": "python", 241 | "nbconvert_exporter": "python", 242 | "pygments_lexer": "ipython3", 243 | "version": "3.8.8" 244 | } 245 | }, 246 | "nbformat": 4, 247 | "nbformat_minor": 4 248 | } 249 | -------------------------------------------------------------------------------- /1. Introduction to TensorFlow/week 2/Graded Assignment/week2-quiz.md: -------------------------------------------------------------------------------- 1 | # Graded Assignment - Quiz 2 2 | 3 |
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1 | # Introduction to Computer Vision
2 |
3 | Computer vision is the field of having a computer understand and label what is present in an image. Can you figure out how can we tell the computer to recognize fashion image? Yes, we use lots of pictures of clothing and tell the computer what that's a picture of and then have the computer figure out the patterns that give you the difference between a shoe, and a shirt, and a handbag, and a coat
4 |
5 | ## Fashion MNIST Dataset
6 | The [Fashion MNIST Dataset](https://github.com/zalandoresearch/fashion-mnist) is a collection of grayscale 28x28 pixel clothing images.
7 |
8 |
9 | Image 1. Fashion Images Dataset
10 |
53 |
54 | Image 2. Neural Network Layers
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/1. Introduction to TensorFlow/week 3/README.md:
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1 | # Enhancing Vision with Convolutional Neural Networks
2 |
3 | ## What are convolutions and pooling?
4 | * Some convolutions will change the image in such a way that certain features in the image get emphasized
5 | * Pooling is a way of compressing an image
6 |
7 | ```python
8 | model = tf.keras.models.Sequential([
9 | # input layer in the shape of our data
10 | tf.keras.layers.Flatten(), # input_shape=(28,28)
11 | # hidden layer
12 | tf.keras.layers.Dense(128, activation=tf.nn.relu),
13 | # output layer in the shape of the number categories
14 | tf.keras.layers.Dense(10, activation=tf.nn.softmax)
15 | ])
16 | ```
17 |
18 | ## Implementing convolutional layers
19 |
20 | ```python
21 | model = tf.keras..models.Sequential([
22 | tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28,28,1)),
23 | tf.keras.layers.MaxPooling2D(2,2),
24 | tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28,28,1)),
25 | tf.keras.layers.MaxPooling2D(2,2),
26 | tf.keras.layers.Flatten(),
27 | tf.keras.layers.Dense(128, activation='relu'),
28 | tf.keras.layers.Dense(10, activation='softmax')
29 | ])
30 | ```
31 |
32 | ### Conv2D
33 | ```python
34 | tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28,28,1))
35 | ```
36 |
37 | ### MaxPooling2D
38 | ```python
39 | tf.keras.layers.MaxPooling2D(2,2)
40 | ```
41 |
42 | * In max-pooling, we're going to take the maximum value
43 | * It's a two-by-two pool, so for every four pixels, the biggest one will **survive**
44 |
45 | Then, we add another convolutional later and another max-pooling layer and then again, pool to reduce the size. So, by the time the image gets to the flattern to go into the dense layers, it's already **much smaller**.
46 |
47 | ```python
48 | tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28,28,1)),
49 | tf.keras.layers.MaxPooling2D(2,2),
50 | tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28,28,1)),
51 | tf.keras.layers.MaxPooling2D(2,2),
52 | ```
53 |
54 | So, its content has been greatly simplified, the goal being that the convolutions will filter it to the features that determine the output.
55 |
56 | ```python
57 | model.summary()
58 | ```
59 |
60 | ## Other Notes
61 | **Overfitting** occurs when the network learns the data from the training set really well, but it's too specialised to only that data, and as a result is less effective at interpreting other unseen data. For example, if all your life you only saw red shoes, then when you see a red shoe you would be very good at identifying it. But blue suede shoes might confuse you.
62 |
63 | ## Ungraded Lab
64 | * Lab 1: [Improving accuracy using convolution](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C1/W3/ungraded_labs/C1_W3_Lab_1_improving_accuracy_using_convolutions.ipynb)
65 | * Lab 2: [Exploring Convolutions](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C1/W3/ungraded_labs/C1_W3_Lab_2_exploring_convolutions.ipynb)
66 |
67 | ## Graded Assignments
68 | * Quiz 3: [week-3-quiz](Graded%20Assignment/week3-quiz.md)
69 | * Programming assignment: [notebook](Graded%20Assignment/)
70 |
71 | ## Reference
72 | * [Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D)
73 | * [MaxPooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D)
74 | * [Convolution Neural Network - Youtube](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF)
75 | * [Image Filtering](https://lodev.org/cgtutor/filtering.html)
76 |
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1 | # Graded Assignment - Quiz 4
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1 | # Using Real-world Images
2 |
3 | ## Understanding ImageDataGenerator
4 | ```python
5 | import tensorflow as tf
6 | from tf.keras.preprocessing.image import ImageDataGenerator
7 |
8 | # instantiate an image generator
9 | # pass rescale to normalize the data
10 | train_datagen = ImageDataGenerator(rescale=1./255)
11 |
12 | # point out directory that contains sub-directories that contain images
13 | # the name of sub-directories will be the labels for the images
14 | train_generator = train_datagen.flow_from_directory(
15 | train_dir,
16 | target_size=(300, 300), # input data all has to be the same size
17 | batch_size=128,
18 | class_mode='binary' # binary classifier
19 | )
20 |
21 | test_datagen = ImageDataGenerator(rescale=1./255)
22 |
23 | validation_generator = test_datagen.flow_from_directory(
24 | validation_dir,
25 | target_size=(300, 300),
26 | batch_size=32,
27 | class_mode='binary'
28 | )
29 | ```
30 |
31 | * The images are resized as they are loaded so we don't need to do preprocessing
32 | * Batch size is a term used in machine learning and refers to the number of training examples utilized in one iteration.
33 |
34 | ## Defining a ConvNet to use complex images
35 | Here, we use three sets of convolution pooling layers.
36 | ```python
37 | model = tf.keras.models.Sequential([
38 | tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(300, 300, 3)),
39 | tf.keras.layers.MaxPooling2D(2, 2),
40 | tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
41 | tf.keras.layers.MaxPooling2D(2, 2),
42 | tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
43 | tf.keras.layers.MaxPooling2D(2, 2),
44 | tf.keras.layers.Flatten(),
45 | tf.keras.layers.Dense(512, activation='relu'),
46 | tf.keras.layers.Dense(1, activation='sigmoid')
47 | ])
48 | ```
49 |
50 | In input shape, because the images are in color we use 3 bytes per pixel for red, green and blue.
51 | ```python
52 | tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(300, 300, 3)),
53 | ```
54 |
55 | For the output layer, we have one neuron for two classes. It because we use different activation. Sigmoid is great for binary classification, where one class will tend towards zero and the other class tending towards one. We could use two neurons here too, and the same softmax function as before, but for binary this is a bit more efficient.
56 | ```python
57 | tf.keras.layers.Dense(1, activation='sigmoid')
58 | ```
59 |
60 | ## Training The ConvNet
61 | ```python
62 | from tensorflow.keras.optimizers import RMSprop
63 |
64 | model.compile(
65 | loss='binary_crossentropy',
66 | optimizer=RMSprop(lr=0.001),
67 | metrics=['accuracy']
68 | )
69 |
70 | history = model.fit(
71 | train_generator,
72 | steps_per_epoch=8,
73 | epochs=15,
74 | validation_data=validation_generator,
75 | validation_steps=8,
76 | verbose=2
77 | )
78 | ```
79 |
80 | * There are 1,024 images in the training directory, we're loading them in 128 at a time. In order to load them all, we need to do 8 batches so we set **steps_per_epoch** to cover that.
81 | * We have 256 images from validation_genertaor and we wanted to handle them in batches of 32, so we will do 8 steps.
82 | * Verbose specifies how much to display while training is going on. With verbose set to 2, we'll get a little less animation hiding the epoch progress.
83 |
84 | ```python
85 | import numpy as np
86 | from google.colab import files
87 | from keras.preprocessing import image
88 |
89 | uploaded = files.upload()
90 |
91 | for fn in uploaded.keys():
92 |
93 | # predicting images
94 | path = '/content/' + fn
95 | img = image.load_img(path, target_size=(300, 300))
96 | x = image.img_to_array(img)
97 | x = np.expand_dims(x, axis=0)
98 |
99 | images = np.vstack([x])
100 | classes = model.predict(images, batch_size=10)
101 | print(classes[0])
102 | if classes[0]>0.5:
103 | print(fn + " is a human")
104 | else:
105 | print(fn + " is a horse")
106 | ```
107 |
108 | This give us the button that can be pressed to pick one or more images to upload.
109 | ```python
110 | ...
111 | from google.colab import files
112 |
113 | uploaded = files.upload()
114 |
115 | for fn in uploaded.keys():
116 | ...
117 | ...
118 | ```
119 |
120 | The loop then iterates through all of the images in that collection.
121 | ```python
122 | img = image.load_img(path, target_size=(300, 300))
123 | x = image.img_to_array(img)
124 | x = np.expand_dims(x, axis=0)
125 |
126 | images = np.vstack([x])
127 | ```
128 |
129 | ## Ungraded Lab
130 | * Lab 1: [Training with ImageDataGenerator](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C1/W4/ungraded_labs/C1_W4_Lab_1_image_generator_no_validation.ipynb)
131 | * Lab 2: [ImageDataGenerator with Validation Set](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C1/W4/ungraded_labs/C1_W4_Lab_2_image_generator_with_validation.ipynb)
132 |
133 | ## References
134 | * [EarlyStopping callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping)
135 | * [Binary Classification](https://www.youtube.com/watch?v=eqEc66RFY0I&t=6s)
136 | * [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/descending-into-ml/video-lecture)
137 |
138 |
139 |
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "id": "1bed6a4c",
6 | "metadata": {},
7 | "source": [
8 | "# Week 1: Explore the BBC News archive\n",
9 | "\n",
10 | "Welcome! In this assignment you will be working with a variation of the [BBC News Classification Dataset](https://www.kaggle.com/c/learn-ai-bbc/overview), which contains 2225 examples of news articles with their respective categories (labels).\n",
11 | "\n",
12 | "Let's get started!"
13 | ]
14 | },
15 | {
16 | "cell_type": "code",
17 | "execution_count": 4,
18 | "id": "64ace5d7",
19 | "metadata": {
20 | "colab": {
21 | "base_uri": "https://localhost:8080/"
22 | },
23 | "id": "zrZevCPJ92HG",
24 | "outputId": "4ba10d10-1433-42fc-dc8e-83b297705cfe"
25 | },
26 | "outputs": [],
27 | "source": [
28 | "import csv\n",
29 | "from tensorflow.keras.preprocessing.text import Tokenizer\n",
30 | "from tensorflow.keras.preprocessing.sequence import pad_sequences"
31 | ]
32 | },
33 | {
34 | "cell_type": "markdown",
35 | "id": "7dcc6e36",
36 | "metadata": {},
37 | "source": [
38 | "Begin by looking at the structure of the csv that contains the data:"
39 | ]
40 | },
41 | {
42 | "cell_type": "code",
43 | "execution_count": 5,
44 | "id": "531133cd",
45 | "metadata": {},
46 | "outputs": [
47 | {
48 | "name": "stdout",
49 | "output_type": "stream",
50 | "text": [
51 | "First line (header) looks like this:\n",
52 | "\n",
53 | "category,text\n",
54 | "\n",
55 | "Each data point looks like this:\n",
56 | "\n",
57 | "tech,tv future in the hands of viewers with home theatre systems plasma high-definition tvs and digital video recorders moving into the living room the way people watch tv will be radically different in five years time. that is according to an expert panel which gathered at the annual consumer electronics show in las vegas to discuss how these new technologies will impact one of our favourite pastimes. with the us leading the trend programmes and other content will be delivered to viewers via home networks through cable satellite telecoms companies and broadband service providers to front rooms and portable devices. one of the most talked-about technologies of ces has been digital and personal video recorders (dvr and pvr). these set-top boxes like the us s tivo and the uk s sky+ system allow people to record store play pause and forward wind tv programmes when they want. essentially the technology allows for much more personalised tv. they are also being built-in to high-definition tv sets which are big business in japan and the us but slower to take off in europe because of the lack of high-definition programming. not only can people forward wind through adverts they can also forget about abiding by network and channel schedules putting together their own a-la-carte entertainment. but some us networks and cable and satellite companies are worried about what it means for them in terms of advertising revenues as well as brand identity and viewer loyalty to channels. although the us leads in this technology at the moment it is also a concern that is being raised in europe particularly with the growing uptake of services like sky+. what happens here today we will see in nine months to a years time in the uk adam hume the bbc broadcast s futurologist told the bbc news website. for the likes of the bbc there are no issues of lost advertising revenue yet. it is a more pressing issue at the moment for commercial uk broadcasters but brand loyalty is important for everyone. we will be talking more about content brands rather than network brands said tim hanlon from brand communications firm starcom mediavest. the reality is that with broadband connections anybody can be the producer of content. he added: the challenge now is that it is hard to promote a programme with so much choice. what this means said stacey jolna senior vice president of tv guide tv group is that the way people find the content they want to watch has to be simplified for tv viewers. it means that networks in us terms or channels could take a leaf out of google s book and be the search engine of the future instead of the scheduler to help people find what they want to watch. this kind of channel model might work for the younger ipod generation which is used to taking control of their gadgets and what they play on them. but it might not suit everyone the panel recognised. older generations are more comfortable with familiar schedules and channel brands because they know what they are getting. they perhaps do not want so much of the choice put into their hands mr hanlon suggested. on the other end you have the kids just out of diapers who are pushing buttons already - everything is possible and available to them said mr hanlon. ultimately the consumer will tell the market they want. of the 50 000 new gadgets and technologies being showcased at ces many of them are about enhancing the tv-watching experience. high-definition tv sets are everywhere and many new models of lcd (liquid crystal display) tvs have been launched with dvr capability built into them instead of being external boxes. one such example launched at the show is humax s 26-inch lcd tv with an 80-hour tivo dvr and dvd recorder. one of the us s biggest satellite tv companies directtv has even launched its own branded dvr at the show with 100-hours of recording capability instant replay and a search function. the set can pause and rewind tv for up to 90 hours. and microsoft chief bill gates announced in his pre-show keynote speech a partnership with tivo called tivotogo which means people can play recorded programmes on windows pcs and mobile devices. all these reflect the increasing trend of freeing up multimedia so that people can watch what they want when they want.\n",
58 | "\n"
59 | ]
60 | }
61 | ],
62 | "source": [
63 | "with open(\"./bbc-text.csv\", 'r') as csvfile:\n",
64 | " print(f\"First line (header) looks like this:\\n\\n{csvfile.readline()}\")\n",
65 | " print(f\"Each data point looks like this:\\n\\n{csvfile.readline()}\") "
66 | ]
67 | },
68 | {
69 | "cell_type": "markdown",
70 | "id": "67a2c94f",
71 | "metadata": {},
72 | "source": [
73 | "As you can see, each data point is composed of the category of the news article followed by a comma and then the actual text of the article."
74 | ]
75 | },
76 | {
77 | "cell_type": "markdown",
78 | "id": "3c61761e",
79 | "metadata": {},
80 | "source": [
81 | "## Removing Stopwords\n",
82 | "\n",
83 | "One important step when working with text data is to remove the **stopwords** from it. These are the most common words in the language and they rarely provide useful information for the classification process.\n",
84 | "\n",
85 | "Complete the `remove_stopwords` below. This function should receive a string and return another string that excludes all of the stopwords provided."
86 | ]
87 | },
88 | {
89 | "cell_type": "code",
90 | "execution_count": 6,
91 | "id": "3336c693",
92 | "metadata": {},
93 | "outputs": [],
94 | "source": [
95 | "# GRADED FUNCTION: remove_stopwords\n",
96 | "def remove_stopwords(sentence):\n",
97 | " # List of stopwords\n",
98 | " stopwords = [\"a\", \"about\", \"above\", \"after\", \"again\", \"against\", \"all\", \"am\", \"an\", \"and\", \"any\", \"are\", \"as\", \"at\", \"be\", \"because\", \"been\", \"before\", \"being\", \"below\", \"between\", \"both\", \"but\", \"by\", \"could\", \"did\", \"do\", \"does\", \"doing\", \"down\", \"during\", \"each\", \"few\", \"for\", \"from\", \"further\", \"had\", \"has\", \"have\", \"having\", \"he\", \"he'd\", \"he'll\", \"he's\", \"her\", \"here\", \"here's\", \"hers\", \"herself\", \"him\", \"himself\", \"his\", \"how\", \"how's\", \"i\", \"i'd\", \"i'll\", \"i'm\", \"i've\", \"if\", \"in\", \"into\", \"is\", \"it\", \"it's\", \"its\", \"itself\", \"let's\", \"me\", \"more\", \"most\", \"my\", \"myself\", \"nor\", \"of\", \"on\", \"once\", \"only\", \"or\", \"other\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"over\", \"own\", \"same\", \"she\", \"she'd\", \"she'll\", \"she's\", \"should\", \"so\", \"some\", \"such\", \"than\", \"that\", \"that's\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"there\", \"there's\", \"these\", \"they\", \"they'd\", \"they'll\", \"they're\", \"they've\", \"this\", \"those\", \"through\", \"to\", \"too\", \"under\", \"until\", \"up\", \"very\", \"was\", \"we\", \"we'd\", \"we'll\", \"we're\", \"we've\", \"were\", \"what\", \"what's\", \"when\", \"when's\", \"where\", \"where's\", \"which\", \"while\", \"who\", \"who's\", \"whom\", \"why\", \"why's\", \"with\", \"would\", \"you\", \"you'd\", \"you'll\", \"you're\", \"you've\", \"your\", \"yours\", \"yourself\", \"yourselves\" ]\n",
99 | " \n",
100 | " # Sentence converted to lowercase-only\n",
101 | " sentence = sentence.lower()\n",
102 | " \n",
103 | " ### START CODE HERE\n",
104 | " sentence = sentence.split()\n",
105 | " temp_sentence = []\n",
106 | " for word in sentence:\n",
107 | " if word not in stopwords:\n",
108 | " temp_sentence.append(word)\n",
109 | " \n",
110 | " sentence = \" \".join(temp_sentence)\n",
111 | " \n",
112 | "# print(temp_sentence)\n",
113 | " ### END CODE HERE\n",
114 | " return sentence"
115 | ]
116 | },
117 | {
118 | "cell_type": "code",
119 | "execution_count": 7,
120 | "id": "857d2bce",
121 | "metadata": {},
122 | "outputs": [
123 | {
124 | "data": {
125 | "text/plain": [
126 | "'go store get snack'"
127 | ]
128 | },
129 | "execution_count": 7,
130 | "metadata": {},
131 | "output_type": "execute_result"
132 | }
133 | ],
134 | "source": [
135 | "# Test your function\n",
136 | "remove_stopwords(\"I am about to go to the store and get any snack\")"
137 | ]
138 | },
139 | {
140 | "cell_type": "markdown",
141 | "id": "69317cd6",
142 | "metadata": {},
143 | "source": [
144 | "***Expected Output:***\n",
145 | "```\n",
146 | "'go store get snack'\n",
147 | "\n",
148 | "```"
149 | ]
150 | },
151 | {
152 | "cell_type": "markdown",
153 | "id": "e8c5c829",
154 | "metadata": {},
155 | "source": [
156 | "## Reading the raw data\n",
157 | "\n",
158 | "Now you need to read the data from the csv file. To do so, complete the `parse_data_from_file` function.\n",
159 | "\n",
160 | "A couple of things to note:\n",
161 | "- You should omit the first line as it contains the headers and not data points.\n",
162 | "- There is no need to save the data points as numpy arrays, regular lists is fine.\n",
163 | "- To read from csv files use [`csv.reader`](https://docs.python.org/3/library/csv.html#csv.reader) by passing the appropriate arguments.\n",
164 | "- `csv.reader` returns an iterable that returns each row in every iteration. So the label can be accessed via row[0] and the text via row[1].\n",
165 | "- Use the `remove_stopwords` function in each sentence."
166 | ]
167 | },
168 | {
169 | "cell_type": "code",
170 | "execution_count": 8,
171 | "id": "c9ae1cd1",
172 | "metadata": {},
173 | "outputs": [],
174 | "source": [
175 | "def parse_data_from_file(filename):\n",
176 | " sentences = []\n",
177 | " labels = []\n",
178 | " with open(filename, 'r') as csvfile:\n",
179 | " ### START CODE HERE\n",
180 | " reader = csv.reader(csvfile, delimiter=',')\n",
181 | " next(reader)\n",
182 | " for row in reader:\n",
183 | " labels.append(row[0])\n",
184 | " sentence = row[1]\n",
185 | " sentences.append(remove_stopwords(sentence))\n",
186 | " \n",
187 | " ### END CODE HERE\n",
188 | " return sentences, labels"
189 | ]
190 | },
191 | {
192 | "cell_type": "code",
193 | "execution_count": 9,
194 | "id": "6a4bb82b",
195 | "metadata": {},
196 | "outputs": [
197 | {
198 | "name": "stdout",
199 | "output_type": "stream",
200 | "text": [
201 | "There are 2225 sentences in the dataset.\n",
202 | "\n",
203 | "First sentence has 436 words (after removing stopwords).\n",
204 | "\n",
205 | "There are 2225 labels in the dataset.\n",
206 | "\n",
207 | "The first 5 labels are ['tech', 'business', 'sport', 'sport', 'entertainment']\n"
208 | ]
209 | }
210 | ],
211 | "source": [
212 | "# Test your function\n",
213 | "sentences, labels = parse_data_from_file(\"./bbc-text.csv\")\n",
214 | "\n",
215 | "print(f\"There are {len(sentences)} sentences in the dataset.\\n\")\n",
216 | "print(f\"First sentence has {len(sentences[0].split())} words (after removing stopwords).\\n\")\n",
217 | "print(f\"There are {len(labels)} labels in the dataset.\\n\")\n",
218 | "print(f\"The first 5 labels are {labels[:5]}\")"
219 | ]
220 | },
221 | {
222 | "cell_type": "markdown",
223 | "id": "1e18ed03",
224 | "metadata": {},
225 | "source": [
226 | "***Expected Output:***\n",
227 | "```\n",
228 | "There are 2225 sentences in the dataset.\n",
229 | "\n",
230 | "First sentence has 436 words (after removing stopwords).\n",
231 | "\n",
232 | "There are 2225 labels in the dataset.\n",
233 | "\n",
234 | "The first 5 labels are ['tech', 'business', 'sport', 'sport', 'entertainment']\n",
235 | "\n",
236 | "```"
237 | ]
238 | },
239 | {
240 | "cell_type": "markdown",
241 | "id": "899468d5",
242 | "metadata": {},
243 | "source": [
244 | "## Using the Tokenizer\n",
245 | "\n",
246 | "Now it is time to tokenize the sentences of the dataset. \n",
247 | "\n",
248 | "Complete the `fit_tokenizer` below. \n",
249 | "\n",
250 | "This function should receive the list of sentences as input and return a [Tokenizer](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer) that has been fitted to those sentences. You should also define the \"Out of Vocabulary\" token as `
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
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1 | # Sentiment in Text
2 |
3 | Unlike images, which come in these regular shaped tensors of pixel intensity values, Text is messier, there are long sentences, there are short sentences. We're going to take a look at what it takes for you to process text because neural networks generally deal in numbers.
4 |
5 | ## Introduction
6 | In the earlier, we have learned about:
7 | * Neural networks and how they can match patterns to perform classifications.
8 | * How to make that a little smarter for images using convolutions to identify the features in the images and classify based on those instead of just matching on raw pixels
9 |
10 | In this course, we'll focus on text and how we can build classifier is based on text models. We'll start by looking at sentiment in text, and learn how to build models that understand text that are trained on labeled text, and then can then classify new text based on what they've seen.
11 |
12 | ## Word Based Encodings
13 | A common simple character encoding is ASCII, the American Standard Code for Information Interchange. But the problem with this of course, is that the semantics of the word aren't encoded in the letters.
14 |
15 | ## Using APIs
16 | ```python
17 | import tensorflow as tf
18 | from tf import keras
19 | from tf.keras.preprocessing.text import Tokenizer
20 |
21 | sentences = [
22 | 'I love my dog',
23 | 'I love my cat',
24 | 'You love my dog'
25 | ]
26 |
27 | # num_words, tokenizer take the top 100 words by volume and encode those
28 | tokenizer = Tokenizer(num_words = 100)
29 |
30 | # take the data and encode it
31 | # generate indices for each word in the corpus
32 | tokenizer.fit_on_texts(sentences)
33 |
34 | # word_index retrun dictionary containing key value pairs
35 | # key is the word and value is token for the word
36 | word_index = tokenizer.word_index
37 | print(word_index)
38 | # print(f'Found {word_index} uniq words')
39 | ```
40 |
41 | By default, Tokenizer skips all punctuations and words are converted to lower case
42 |
43 | ## Text to Sequence
44 | text to sequence is about turn sentences into lists of values based on tokens made before.
45 |
46 | ```python
47 | import tensorflow as tf
48 | from tf import keras
49 | from tf.keras.preprocessing.text import Tokenizer
50 |
51 | sentences = [
52 | 'I love my dog',
53 | 'I love my cat',
54 | 'You love my dog',
55 | 'Do you think my dog is amazing?'
56 | ]
57 |
58 | tokenizer = Tokenizer(num_words = 100, oov_token="
70 |
71 | ## Looking more at the Tokenizer
72 | We really need a lot of training data to get a broad vocabulary. In many cases, it's a good idea to instead of just ignoring unseen words, to put a special value in when an unseen word is encountered. We will use OOV (out of vocabulary) token.
73 |
74 | ```python
75 | import tensorflow as tf
76 | from tf import keras
77 | from tf.keras.preprocessing.text import Tokenizer
78 |
79 | sentences = [
80 | 'I love my dog',
81 | 'I love my cat',
82 | 'You love my dog',
83 | 'Do you think my dog is amazing?'
84 | ]
85 |
86 | tokenizer = Tokenizer(num_words = 100, oov_token="
98 |
99 | ## Padding
100 | Working with texts, we needed to have some level of uniformity of size. So, we will use padding.
101 |
102 | ```python
103 | import tensorflow as tf
104 | from tf import keras
105 | from tf.keras.preprocessing.text import Tokenizer
106 | from tf.keras.preprocessing.sequence import pad_sequences
107 |
108 | sentences = [
109 | 'I love my dog',
110 | 'I love my cat',
111 | 'You love my dog',
112 | 'Do you think my dog is amazing?'
113 | ]
114 |
115 | tokenizer = Tokenizer(num_words = 100, oov_token="
130 |
131 | Padding parameters
132 | ```python
133 | padded = pad_sequences(
134 | sequences,
135 | padding='post',
136 | truncating='post',
137 | maxlen=5)
138 | ```
139 |
140 | * ``padding='post'``, put padding after the sentence
141 | * ``truncating='post'``, lose the beginning of the sentence
142 | * ``maxlen=5``, maximum of 5 words
143 |
144 | When we're training a neural network, we do want to have all the data to be the same size, this is the use of pad_sequences.
145 |
146 | ## Ungraded Labs
147 | * Lab 1: [Tokenizer Basics](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C3/W1/ungraded_labs/C3_W1_Lab_1_tokenize_basic.ipynb)
148 | * Lab 2: [Generating Sequences and Padding](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C3/W1/ungraded_labs/C3_W1_Lab_2_sequences_basic.ipynb)
149 | * Lab 3: [Tokenizing the Sarcasm Dataset](https://github.com/https-deeplearning-ai/tensorflow-1-public/blob/main/C3/W1/ungraded_labs/C3_W1_Lab_3_sarcasm.ipynb)
150 |
151 | ## References
152 | * [Text Tokenization](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer#arguments)
153 | * [Assignments](https://charon.me/posts/keras/keras3/)
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1 | # Practice
2 | Dataset: [Sarcsm in News Headlines Dataset by Rishabh Misra](https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection)
3 |
4 | There are three elements in it:
5 | * Sarcastic as label
6 | ```
7 | is_sarcastic: 1 if the record is sarcastic
8 | otherwise 0
9 | ```
10 | * Headline which is just plain text, the headline of the news article
11 | * Article link, link to the original news article
12 |
13 | 
14 |
15 | ```python
16 | import json
17 |
18 | with.open("sarcasm.json", 'r') as f:
19 | datastore = json.load(f)
20 |
21 | sentences = []
22 | labels = []
23 | urls = []
24 |
25 | for item in datastore:
26 | sentences.append(item['headline'])
27 | labels.append(item['is_sarcastic'])
28 | urls.append(item['article_link'])
29 | ```
30 |
31 | ## Working with Tokenizer
32 |
33 | ```python
34 | from tensorflow.keras.processing.text import Tokenizer
35 | from tensorflow.keras.processing.text import pad_sequences
36 |
37 | tokenizer = Tokenizer(oov_token="
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1 | # Word Embeddings
2 |
3 | One of the coolest things about word embedding is you can download the pretrained word embedding that maybe someone else has trained.
4 |
5 | ## TFDS
6 |
7 | 
8 | Images 1. TensorFlow Data Services
9 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
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17 |
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1 | # Sequence Models
2 |
3 | ## Introduction
4 | The main reason for why our classifier failed to get any meaningful results was that the context of words was hard to follow when the words were broken down into sub-words and the sequence in which the tokens for the sub-words appear becomes very important in understanding their meaning.
5 |
6 |
7 | 
8 | Images 1. Neural Network
9 |
14 | 
15 | Images 2. Function
16 |
21 | 
22 | Images 3. RNN
23 |
31 | 
32 | Images 4. LSTM
33 |
38 | 
39 | Images 5. Bidirection LSTM
40 |
57 | 
58 | Images 6. Model Summary
59 |
75 | 
76 | Images 7. Model Summary 2
77 |
81 | 
82 | Images 8. One Layer and Two Layer LSTM Accuracy Comparison
83 |
87 | 
88 | Images 10. 50 Epoch Accuracy Comparison
89 |
93 | 
94 | Images 10. 50 Epoch Loss Comparison
95 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
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15 |
16 |
17 |
18 |
19 | 
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1 | # Sequences and Prediction
2 |
3 | Take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!
4 |
5 | ## Introduction
6 | Time-series is one part of sequence models where it's a case of if you can imagine a series of data that changes over time. It might be the closing prices for stock on the stock exchange, or it could be weather. It could be how sunny it is in California on a given day, or how rainy it is in Seattle on a given day, that type of thing. So if you just imagine how an item of data changes over time and how it's measured over time.
7 |
8 | We're going to start by creating a synthetic sequence of data, so that we can start looking at what the common attributes that you see in data series are. So for example:
9 | * Data can be seasonal. It's sunnier in June than it is in January or it's wetter in November than it is in October, something along those lines. So you have that seasonality of data.
10 | * Data can have trends, like whether it probably doesn't really trend although we could argue that it strangely enough idea with climate change, but like a stock data may trend upwards over time or downwards over some other times, and then of course the random factor that makes it hard to predict is noise.
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12 | So you can have like seasonal data, you can have trends in your data, but then you can have noise on that data as well.
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14 | ## Time Series Example
15 | Different type of time series, looking at basic forecasting around them. Time series are everywhere. You may have seen them in stock prices, weather forecasts, historical trends, such as Moore's law. What exactly is a time series? It's typically defined as an ordered sequence of values that are usually equally spaced over time. So for example, every year in my Moore's law charts or every day in the weather forecast.
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17 | In each of these examples, there is a single value at each time step, and as a results, the term **univariate** is used to describe them. You may also encounter time series that have multiple values at each time step. As you might expect, they're called **Multivariate Time Series**.
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19 | Multivariate Time Series charts can be useful ways of understanding the impact of related data. For example, consider this chart of births versus deaths in Japan from 1950 to 2008. It clearly shows the two converging, but then deaths begin to outstrip births leading to a population decline. Now, while they could be treated as two separate univariate time series, the real value of the data becomes apparent when we show them together as a multivariate.
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1 | # Graded Assignment - Quiz 3
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1 | # Recurrent Neural Networks for Time Series
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3 | Recurrent Neural networks (RNN) and Long Short Term Memory (LSTM) networks are really useful to classify and predict on sequential data.
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5 | ### Sequence Data
6 | Sequential Data is any kind of data where the order matters as you said. So we can assume that time series is a kind of sequential data, because the order matters.
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8 | ### Sequence Model
9 | Sequence models are the machine learning models that input or output sequences of data. Sequential data includes text streams, audio clips, video clips, time-series data and etc.
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11 | ### Lambda Layers
12 | Lambda layers allow us to write effectively an arbitrary piece of code as a layer in the neural network. Basically a Lambda function, an unnamed function, but implemented as a layer in the neural network that resend the data, scales it. More simply we can say that using the lambda layer we can transform the data before applying that data to any of the existing layers.
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14 | ## Conceptual Overview
15 | One difference will be that the full input shape when using RNNs is three-dimensional. The first dimension will be the batch size, the second will be the timestamps, and the third is the dimensionality of the inputs at each time step.
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/README.md:
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1 | # DeepLearning.AI TensorFlow Developer Coursera
2 |
3 | This repository was created to collect my self-notes, codes and solutions of every videos / reading materials / questions / quizes / exercises while learning [TensorFlow Developer course](https://www.coursera.org/professional-certificates/tensorflow-in-practice) on Coursera by DeepLearning.AI taught by [Laurence Moroney](https://www.coursera.org/instructor/lmoroney).
4 |
5 | ## TensorFlow
6 | TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.
7 |
8 | ## Course Topics
9 | The topics covered in this course are:
10 | 1. [Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning](https://www.coursera.org/learn/introduction-tensorflow)
11 | * [Week 1](1.%20Introduction%20to%20TensorFlow/week%201/) A New Programming Paradigm
12 | * [Week 2](1.%20Introduction%20to%20TensorFlow/week%202/) Introduction to Computer Vision
13 | * [Week 3](1.%20Introduction%20to%20TensorFlow/week%203/) Enhancing Vision with Convolutional Neural Networks
14 | * [Week 4](1.%20Introduction%20to%20TensorFlow/week%204/) Using Real-world Images
15 | 2. [Convolutional Neural Networks in TensorFlow](https://www.coursera.org/learn/convolutional-neural-networks-tensorflow)
16 | * [Week 1]() Exploring a Larger Dataset
17 | * [Week 2]() Augmentation: A technique to avoid overfitting
18 | * [Week 3]() Transfer Learning
19 | * [Week 4]() Multiclass Classifications
20 | 3. [Natural Language Processing in TensorFlow](https://www.coursera.org/learn/natural-language-processing-tensorflow)
21 | * [Week 1]() Sentiment in text
22 | * [Week 2]() Word Embeddings
23 | * [Week 3]() Sequence models
24 | * [Week 4]() Sequence models and literature
25 | 4. [Sequences, Time Series and Prediction](https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction)
26 | * [Week 1]() Sequences and Prediction
27 | * [Week 2]() Deep Neural Networks for Time Series
28 | * [Week 3]() Recurrent Neural Networks for Time Series
29 | * [Week 4]() Real-world time series data
30 |
31 | ## Certificate of Completion
32 | coming-soon.
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