├── LICENSE ├── PadhAI-Download-Materials ├── 0202_GoogleColab-1549119762873.ipynb ├── 0202_PythonBasics-1549119762879.ipynb ├── 0207_MorePythonBasics-1549598493600.ipynb ├── 0207_Vectors-1549598493596.ipynb ├── 0214_MPNeuronAndPerceptron-1550141205767.ipynb ├── 0225_SigmoidNeuron-1551169534921.ipynb ├── 0228_SigmoidNeuron-1551253091703.ipynb ├── 0318_FeedForwardNetwork_new-1553058835338.ipynb ├── 0324_ScalarBackpropagation-1553414815802.ipynb ├── 0328_VectorizedFeedForwardNetworks-1553747879251.ipynb ├── 0404_GDAlgorithms-1554543997405.ipynb ├── 0407_GDAlgorithms-1554564131435.ipynb ├── 0407_VectorisedGDAlgorithms-1554564131433.ipynb ├── 0413_InitialisationActivationFunctions-1555126885979.ipynb ├── 0417_OverfittingAndRegularisation-1555486774539.ipynb ├── 0420_PytorchIntro-1555587538086.ipynb ├── 0422_FFNetworksWithPyTorch-1555679700514.ipynb ├── 0429_PyTorchCNN-1556352430467.ipynb ├── 0505_LargeCNNs-1557218705862.ipynb ├── 0511_CNNVisualisation-1557554669715.ipynb ├── 0516_BatchNorm_Dropout-1557985188257.ipynb ├── 0518_HyperparameterTuning_MLFlow-1558166961869.ipynb ├── 0715_BatchSeqModels.ipynb ├── 0721_EncoderDecoderArchitecture-1563719382708.ipynb ├── Contest-1.2-Text_Non_Text_Classification.ipynb ├── Contest-1.3-Text_Non_Text_Classification.ipynb ├── Contest_1_1_Solution_Sample-1550240301484.ipynb ├── FFNetworkMultiClass-1553058835337.png ├── FFNetworkSingle-1552984072224.png ├── FirstNetwork-1553414815807.png ├── FirstNetwork-1553747879260.png ├── NEWS2012-Ref-EnHi-1000.xml ├── NEWS2012-Training-EnHi-13937.xml ├── NetworkForExercise-1553414815807.png ├── SecondNetwork-1553414815809.png ├── SecondNetwork-1553747879261.png ├── SimpleNetwork-1553058835338.png ├── _07_06_RNNs-1562395440877.ipynb ├── data-1557554654170.zip ├── mobile_cleaned-1549119762886.csv ├── mobile_cleaned-1551253091700.csv ├── name2lang.txt ├── weights_viz-1553414815805.gif └── weights_viz_multi_class-1553414815806.gif ├── README.md ├── certificates ├── DL101-GuviCertification-u3793g5r7n16y5M2U2.pdf ├── DL102-GuviCertification-54n71RC9Y3s932y3z7.pdf ├── DL103-GuviCertification-50Sw413d7r9T98L75U.pdf ├── DL104-GuviCertification-4775163N3z0T5DpE95.pdf ├── DL105-GuviCertification-3219Q8mV9H75oPpJ57.pdf ├── DL106-GuviCertification-551019LF6680gU47eh.pdf ├── DL107-GuviCertification-4l5917H170YAC689z5.pdf ├── DL108-GuviCertification-590umI28b179s79Pve.pdf ├── DL109-GuviCertification-3T6hY1RK7v75t25u42.pdf ├── DL110-GuviCertification-19g891p7375h721VlT.pdf ├── DL111-GuviCertification-0A5R4s77281t16V628.pdf ├── DL112-GuviCertification-wLX9m77i0H6S7819M5.pdf ├── DeepLearningCertificate.pdf └── certificates_all.pdf ├── colab-notebooks ├── 01_IntroToGoogleColab.ipynb ├── 02_MorePythonBasics.ipynb ├── 03_Vector.ipynb ├── 04_MPNeuron.ipynb ├── 05_SigmoidNeuron.ipynb ├── 06_SigmoidNeuronExample.ipynb ├── 07_FeedForwardNeuralNetwork.ipynb ├── 08_Scalar_Backpropagation.ipynb ├── 09_VectorizedFeedforwardNeuralNetwork.ipynb ├── 10_GDAlgorithms.ipynb ├── 11_VectorizedGDAlgorithms.ipynb ├── 12_InitializationActivationFunction.ipynb ├── 13_OverfittingAndRegularization.ipynb ├── 14_PyTorchIntro.ipynb ├── 15_FFNetworksWithPyTorch.ipynb ├── 16_PyTorchCNN.ipynb ├── 17_LargeCNNs.ipynb ├── 18_CNNVisualization.ipynb ├── 19_BatchNorm_Dropout.ipynb ├── 20_HyperparameterTuning_MLFlow.ipynb ├── 21_RNNs.ipynb ├── 22_BatchSeqModels.ipynb ├── 23_EncoderDecoderArchitecture.ipynb └── data-sets │ ├── data.zip │ ├── imagenet │ ├── 1 │ │ ├── dome538-1.JPEG │ │ ├── dome538-2.JPEG │ │ ├── harmonica593-1.JPEG │ │ ├── harmonica593-2.JPEG │ │ ├── harmonica593-3.JPEG │ │ ├── harmonica593-4.JPEG │ │ ├── stethoscope823-1.JPEG │ │ └── stethoscope823-2.JPEG │ └── _DS_Store │ ├── imagenet_labels.txt │ ├── mobile_cleaned.csv │ ├── mobile_cleaned_v1.csv │ └── mobile_cleaned_v2.csv ├── extra-notes ├── Lecture1.pdf ├── Lecture10.pdf ├── Lecture11.pdf ├── Lecture12.pdf ├── Lecture13.pdf ├── Lecture14.pdf ├── Lecture15.pdf ├── Lecture16.pdf ├── Lecture17.pdf ├── Lecture18.pdf ├── Lecture19.pdf ├── Lecture2.pdf ├── Lecture20.pdf ├── Lecture21.pdf ├── Lecture22.pdf ├── Lecture23.pdf ├── Lecture3.pdf ├── Lecture4.pdf ├── Lecture5.pdf ├── Lecture6.pdf ├── Lecture7.pdf ├── Lecture8.pdf └── Lecture9.pdf └── notes ├── 10. Sequence Models ├── GuviCertification - 19g891p7375h721VlT.pdf ├── Long+Short+Term+Memory.pdf ├── Recurrent+Neural+Networks.pdf ├── Sequence+Learning+Problems.pdf ├── Vanishing+and+Exploding+Gradients.pdf └── Vanishing+and+Exploding+gradients+in+LSTM.pdf ├── 11. Encoder Decoder Models ├── Encoder+Decoder+Models.pdf └── GuviCertification - 0A5R4s77281t16V628.pdf ├── 13. Capstone Project ├── ProjectDetails.txt └── Signboard Translation from Vernacular Languages — AI4Bharat.pdf ├── 3. Sigmoid Neuron ├── Contest_1_1_Solution_Sample.ipynb ├── Lesson+10_+Learning+-+Learning+by+guessing.pdf ├── Lesson+11_+Data+&+Task.pdf ├── Lesson+11_+Learning+-+Error+Surfaces+for+learning.pdf ├── Lesson+12_+Learning+-+Mathematical+setup+for+the+learning+algorithm.pdf ├── Lesson+12_+Model.pdf ├── Lesson+15_+Learning+-+More+intuitions+about+Taylor+series.pdf ├── Lesson+16_+Learning+-+Deriving+the+Gradient+Descent+Update+Rule.pdf ├── Lesson+16_+Learning+Algorithm.pdf ├── Lesson+17_+Learning+-+Why+it+works_.pdf ├── Lesson+18_+Learning+-+Computing+Partial+Derivatives.pdf ├── Lesson+18_+Learning+-+Will+It+Always+Work_.pdf ├── Lesson+19_+Learning+-+Writing+the+code.pdf ├── Lesson+20_+Sigmoid+-+Dealing+with+more+than+2+parameters.pdf ├── Lesson+21_+Sigmoid+-+Evaluation.pdf ├── Lesson+22_+Summary+and+take-aways.pdf ├── Lesson+2_+Revisiting+limitations+of+perceptron+model.pdf ├── Lesson+46_+Certain+Events.pdf ├── Lesson+47_+Why+do+we+care+about+distributions_.pdf ├── Lesson+48_+Information+Theory+Expectation.pdf ├── Lesson+49_+Information+Content.pdf ├── Lesson+50_+Entropy.pdf ├── Lesson+51_+Relation+To+Number+Of+Bits.pdf ├── Lesson+52_+KL-Divergence+and+Cross+Entropy.pdf ├── Lesson+53_+Sigmoid+Neuron+and+Cross+Entropy.pdf ├── Lesson+54_+Using+Cross+Entropy+With+Sigmoid+Neuron.pdf ├── Lesson+55_+Learning+Algorithm+for+Cross+Entropy+loss+function.pdf ├── Lesson+57_+Code+for+Cross+Entropy+Loss+function.pdf ├── Lesson+5_+Sigmoid+Model+Part+3.pdf ├── Lesson+8_+Sigmoid+-+Loss+Function.pdf └── Text - Non Text Classification.ipynb ├── 4. Feedforward Neural Network ├── FFNetworkMultiClass.png ├── FFNetworkSingle.png ├── Lesson+10_+Model+-+Understanding+the+computations+in+a+deep+neural+network.pdf ├── Lesson+11_+Model+-+The+output+layer+of+a+deep+neural+network.pdf ├── Lesson+12_+Model+-+Output+layer+of+a+multi-class+classification+problem.pdf ├── Lesson+13_+Model+-+How+do+you+choose+the+right+network+configuration.pdf ├── Lesson+14_+Loss+function+for+binary+classification.pdf ├── Lesson+15_+Loss+function+for+multi-class+classification.pdf ├── Lesson+16_+Learning+Algorithm+(non-mathy+version).pdf ├── Lesson+17_+Evaluation.pdf ├── Lesson+18_+Summary.pdf ├── Lesson+1_+Why+do+we+need+complex+functions_.pdf ├── Lesson+2_+Complex+functions+in+real+world+examples.pdf ├── Lesson+3_+A+simple+recipe+for+building+complex+functions.pdf ├── Lesson+4_+Illustrative+Proof+of+Universal+Approximation+Theorem.pdf ├── Lesson+5_+Summary.pdf ├── Lesson+7_+Data+and+Tasks.pdf ├── Lesson+8_+Model+-+A+simple+deep+neural+network.pdf ├── Lesson+9_+Model+-+A+generic+deep+neural+network.pdf └── SimpleNetwork.png ├── 5. Training Feedforward Neural Network ├── 0328_VectorizedFeedForwardNetworks.ipynb ├── FirstNetwork.png ├── GuviCertification - 3219Q8mV9H75oPpJ57.pdf ├── Lesson+10_+Takeaways+and+what+next_.pdf ├── Lesson+1_+Setting+the+context.pdf ├── Lesson+21_+Setting+the+Context.pdf ├── Lesson+22_+Intuition+behind+backpropagation.pdf ├── Lesson+23_+Understanding+the+dimensions+of+gradients (1).pdf ├── Lesson+24_+Computing+Derivatives+w.r.t.+Output+Layer+-+Part+1.pdf ├── Lesson+25_+Computing+Derivatives+w.r.t.+Output+Layer+-+Part+2.pdf ├── Lesson+26_+Computing+Derivatives+w.r.t.+Output+Layer+-+Part+3.pdf ├── Lesson+27_+Quick+recap+of+the+story+so+far.pdf ├── Lesson+28_+Computing+Derivatives+w.r.t.+Hidden+Layers+-+Part+1.pdf ├── Lesson+29_+Computing+Derivatives+w.r.t.+Hidden+Layers+-+Part+2.pdf ├── Lesson+2_+Revisiting+Basic+Calculus.pdf ├── Lesson+30_+Computing+Derivatives+w.r.t.+Hidden+Layers+-+Part+3.pdf ├── Lesson+31_+Computing+derivatives+w.r.t.+one+weight+in+any+layer.pdf ├── Lesson+32_+Computing+derivatives+w.r.t.+all+weights+in+any+layer.pdf ├── Lesson+33_+A+running+example+of+backpropagation.pdf ├── Lesson+34_+Summary.pdf ├── Lesson+3_+Why+do+we+care+about+chain+rule+of+derivatives.pdf ├── Lesson+4_+Applying+chain+rule+across+multiple+paths.pdf ├── Lesson+5_+Applying+Chain+rule+in+a+neural+network.pdf ├── Lesson+6_+Computing+Partial+Derivatives+w.r.t.+a+weight+-+Part+1.pdf ├── Lesson+7_+Computing+Partial+Derivatives+w.r.t.+a+weight+-+Part+2.pdf ├── Lesson+8_+Computing+Partial+Derivatives+w.r.t.+a+weight+-+Part+3.pdf ├── Lesson+9_+Computing+Partial+Derivatives+w.r.t.+a+weight+when+there+are+multiple+paths.pdf ├── NetworkForExercise.png └── SecondNetwork.png ├── 6. Optimization Algorithms ├── GuviCertification - 551019LF6680gU47eh.pdf ├── Lesson+10_+A+disadvantage+of+momentum+based+gradient+descent.pdf ├── Lesson+11_+Intuition+behind+nesterov+accelerated+gradient+descent.pdf ├── Lesson+12_+Running+and+visualizing+nesterov+accelerated+gradient+descent.pdf ├── Lesson+13_+Summary+and+what+next.pdf ├── Lesson+14_+The+idea+of+stochastic+and+mini-batch+gradient+descent.pdf ├── Lesson+15_+Running+stochastic+gradient+descent.pdf ├── Lesson+16_+Running+mini-batch+gradient+descent.pdf ├── Lesson+17_+Epochs+and+Steps.pdf ├── Lesson+18_+Why+do+we+need+an+adaptive+learning+rate+_.pdf ├── Lesson+19_+Introducing+Adagrad.pdf ├── Lesson+1_+A+quick+history+of+DL+to+set+the+context.pdf ├── Lesson+20_+Running+and+Visualizing+Adagrad.pdf ├── Lesson+21_+A+limitation+of+Adagrad.pdf ├── Lesson+22_+Running+and+visualizing+RMSProp.pdf ├── Lesson+23_+Running+and+visualizing+Adam.pdf ├── Lesson+24_+Summary.pdf ├── Lesson+2_+Highlighting+a+limitation+of+Gradient+Descent.pdf ├── Lesson+3_+A+deeper+look+into+the+limitation+of+gradient+descent.pdf ├── Lesson+40_+Setting+the+context.pdf ├── Lesson+41_+Saturation+in+logistic+neuron.pdf ├── Lesson+42_+Zero+centered+functions.pdf ├── Lesson+43_+Introducing+Tanh+and+Relu+activation+functions.pdf ├── Lesson+44_+Tanh+and+ReLU+Activation+Functions.pdf ├── Lesson+45_+Symmetry+Breaking+Problem.pdf ├── Lesson+46_+Xavier+and+He+initialization.pdf ├── Lesson+47_+Summary+and+what+next.pdf ├── Lesson+4_+Introducing+contour+maps.pdf ├── Lesson+56_+Simple+v_s+complex+models.pdf ├── Lesson+57_+Analysing+the+behavior+of+simple+and+complex+models.pdf ├── Lesson+58_+Bias+and+Variance.pdf ├── Lesson+59_+Test+error+due+to+high+bias+and+high+variance.pdf ├── Lesson+5_+Exercise+-+Guess+the+3D+surface.pdf ├── Lesson+60_+Overfitting+in+deep+neural+networks.pdf ├── Lesson+61_+A+detour+into+hyperparameter+tuning.pdf ├── Lesson+62_+L2+regularization.pdf ├── Lesson+63_+Dataset+Augmentation+and+Early+Stopping.pdf ├── Lesson+64_+Summary.pdf ├── Lesson+6_+Visualizing+gradient+descent+on+a+2D+contour+map.pdf ├── Lesson+7_+Intuition+for+momentum+based+gradient+descent.pdf ├── Lesson+8_+Dissecting+the+update+rule+for+momentum+based+gradient+descent.pdf └── Lesson+9_+Running+and+Visualizing+momentum+based+gradient+descent.pdf ├── 8. Convolutional Neural Networks ├── GuviCertification - 590umI28b179s79Pve.pdf ├── Lesson+10_+How+is+the+convolution+operation+related+to+Neural+Networks+-+Part+3.pdf ├── Lesson+11_+Understanding+the+input_output+dimensions.pdf ├── Lesson+12_+Sparse+Connectivity+and+Weight+Sharing.pdf ├── Lesson+13_+Max+Pooling+and+Non-Linearities.pdf ├── Lesson+14_+Our+First+Convolutional+Neural+Network+(CNN).pdf ├── Lesson+15_+Training+CNNs.pdf ├── Lesson+16_+Summary+and+what+next.pdf ├── Lesson+1_+Setting+the+Context.pdf ├── Lesson+2_+The+1D+convolution+operation.pdf ├── Lesson+3_+The+2D+Convolution+Operation.pdf ├── Lesson+4_+Examples+of+2D+convolution.pdf ├── Lesson+5_+2D+convolution+with+a+3D+filter.pdf ├── Lesson+6_+Terminology.pdf ├── Lesson+7_+Padding+and+Stride.pdf ├── Lesson+8_+How+is+the+convolution+operation+related+to+Neural+Networks+-+Part+1.pdf └── Lesson+9_+How+is+the+convolution+operation+related+to+Neural+Networks+-+Part+2.pdf └── 9. Deep Convolutional Neural Networks ├── GuviCertification - 3T6hY1RK7v75t25u42.pdf ├── Lesson+10_+1x1+Convolutions.pdf ├── Lesson+11_+The+Intuition+behind+GoogLeNet.pdf ├── Lesson+12_+The+Inception+Module.pdf ├── Lesson+13_+The+GoogleNet+Architecture.pdf ├── Lesson+14_+Average+Pooling.pdf ├── Lesson+15_+Auxiliary+Loss+for+training+a+deep+network.pdf ├── Lesson+16_+ResNet.pdf ├── Lesson+1_+Setting+the+context (1).pdf ├── Lesson+27_+Receptive+field+of+a+neuron.pdf ├── Lesson+28_+Identifying+images+which+cause+certain+neurons+to+fire.pdf ├── Lesson+29_+Visualising+filters.pdf ├── Lesson+2_+The+Imagenet+Challenge.pdf ├── Lesson+30_+Occlusion+experiments.pdf ├── Lesson+37_+Normalizing+inputs.pdf ├── Lesson+38_+Why+should+we+normalize+the+inputs.pdf ├── Lesson+39_+Batch+Normalization.pdf ├── Lesson+3_+Understanding+the+first+layer+of+AlexNet.pdf ├── Lesson+40_+Learning+Mu+and+Sigma.pdf ├── Lesson+41_+Ensemble+Methods.pdf ├── Lesson+42_+The+idea+of+dropout.pdf ├── Lesson+43_+Training+without+dropout.pdf ├── Lesson+44_+How+does+weight+sharing+help_.pdf ├── Lesson+45_+Using+dropout+at+test+time.pdf ├── Lesson+46_+How+does+dropout+act+as+a+regularizer_.pdf ├── Lesson+47_+Summary+and+what+next_.pdf ├── Lesson+4_+Understanding+all+layers+of+AlexNet.pdf ├── Lesson+5_+ZFNet (1).pdf ├── Lesson+5_+ZFNet.pdf ├── Lesson+6_+VGGNet.pdf ├── Lesson+7_+Summary.pdf ├── Lesson+8_+Setting+the+context.pdf └── Lesson+9_+Number+of+computations+in+a+convolution+layer.pdf /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Satyajit Ghana 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 | -------------------------------------------------------------------------------- /PadhAI-Download-Materials/0202_GoogleColab-1549119762873.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"0202_GoogleColab.ipynb","version":"0.3.2","provenance":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"metadata":{"id":"U3yct2s2Pme9","colab_type":"text"},"cell_type":"markdown","source":["# Section 1\n","Simple Linux commands\n"]},{"metadata":{"id":"pzDl5mOYPhdj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":36},"outputId":"28a409ed-b9fb-485c-e2cb-7ef9c53232f3","executionInfo":{"status":"ok","timestamp":1548591493852,"user_tz":-330,"elapsed":3177,"user":{"displayName":"One Fourth Labs Student","photoUrl":"","userId":"07586129509304884691"}}},"cell_type":"code","source":["!ls"],"execution_count":5,"outputs":[{"output_type":"stream","text":["sample_data\n"],"name":"stdout"}]},{"metadata":{"id":"Syk1BebHPuYe","colab_type":"code","colab":{}},"cell_type":"code","source":[""],"execution_count":0,"outputs":[]},{"metadata":{"id":"PtSBz7pDOAwI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":36},"outputId":"278784d1-2f70-4e75-880a-1a2d69147d0a","executionInfo":{"status":"ok","timestamp":1548591264172,"user_tz":-330,"elapsed":2973,"user":{"displayName":"One Fourth Labs Student","photoUrl":"","userId":"07586129509304884691"}}},"cell_type":"code","source":["!ls"],"execution_count":4,"outputs":[{"output_type":"stream","text":["sample_data\n"],"name":"stdout"}]},{"metadata":{"id":"uMGSTI6COZ11","colab_type":"code","colab":{}},"cell_type":"code","source":["!cat /proc/cpuinfo"],"execution_count":0,"outputs":[]},{"metadata":{"id":"zUszNle1OpUX","colab_type":"code","colab":{}},"cell_type":"code","source":["!cat /proc/meminfo"],"execution_count":0,"outputs":[]},{"metadata":{"id":"VeFHYl-ePWJh","colab_type":"text"},"cell_type":"markdown","source":["This is a text cell, where I can write in **bold** \n","I can also write in $\\LaTeX$ "]},{"metadata":{"id":"FrlMN3ePP1z1","colab_type":"text"},"cell_type":"markdown","source":["# Section 2\n","Moving on to Python"]}]} -------------------------------------------------------------------------------- /PadhAI-Download-Materials/0420_PytorchIntro-1555587538086.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "0420_PytorchIntro.ipynb", 7 | "version": "0.3.2", 8 | "provenance": [], 9 | "collapsed_sections": [] 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | }, 15 | "accelerator": "GPU" 16 | }, 17 | "cells": [ 18 | { 19 | "metadata": { 20 | "id": "KPqFTlOiiiKv", 21 | "colab_type": "text" 22 | }, 23 | "cell_type": "markdown", 24 | "source": [ 25 | "## Outline\n", 26 | "* PyTorch\n", 27 | "* What are tensors\n", 28 | "* Initialising, slicing, reshaping tensors\n", 29 | "* Numpy and PyTorch interfacing\n", 30 | "* GPU support for PyTorch + Enabling GPUs on Google Colab\n", 31 | "* Speed comparisons, Numpy -- PyTorch -- PyTorch on GPU\n", 32 | "* Autodiff concepts and application\n", 33 | "* Writing a basic learning loop using autograd\n", 34 | "* Exercises" 35 | ] 36 | }, 37 | { 38 | "metadata": { 39 | "id": "j4wIFDnRakTz", 40 | "colab_type": "code", 41 | "colab": {} 42 | }, 43 | "cell_type": "code", 44 | "source": [ 45 | "import torch\n", 46 | "import numpy as np\n", 47 | "import matplotlib.pyplot as plt" 48 | ], 49 | "execution_count": 0, 50 | "outputs": [] 51 | }, 52 | { 53 | "metadata": { 54 | "id": "WJyLCI8PcPZq", 55 | "colab_type": "text" 56 | }, 57 | "cell_type": "markdown", 58 | "source": [ 59 | "## Initialise tensors" 60 | ] 61 | }, 62 | { 63 | "metadata": { 64 | "id": "NV2jveDIayX-", 65 | "colab_type": "code", 66 | "colab": {} 67 | }, 68 | "cell_type": "code", 69 | "source": [ 70 | "x = torch.ones(3, 2)\n", 71 | "print(x)\n", 72 | "x = torch.zeros(3, 2)\n", 73 | "print(x)\n", 74 | "x = torch.rand(3, 2)\n", 75 | "print(x)" 76 | ], 77 | "execution_count": 0, 78 | "outputs": [] 79 | }, 80 | { 81 | "metadata": { 82 | "id": "BuAQVNaPFN1P", 83 | "colab_type": "code", 84 | "colab": {} 85 | }, 86 | "cell_type": "code", 87 | "source": [ 88 | "x = torch.empty(3, 2)\n", 89 | "print(x)\n", 90 | "y = torch.zeros_like(x)\n", 91 | "print(y)" 92 | ], 93 | "execution_count": 0, 94 | "outputs": [] 95 | }, 96 | { 97 | "metadata": { 98 | "id": "bWrOMr-hFbwo", 99 | "colab_type": "code", 100 | "colab": {} 101 | }, 102 | "cell_type": "code", 103 | "source": [ 104 | "x = torch.linspace(0, 1, steps=5)\n", 105 | "print(x)" 106 | ], 107 | "execution_count": 0, 108 | "outputs": [] 109 | }, 110 | { 111 | "metadata": { 112 | "id": "l_QspfvYEtuB", 113 | "colab_type": "code", 114 | "colab": {} 115 | }, 116 | "cell_type": "code", 117 | "source": [ 118 | "x = torch.tensor([[1, 2], \n", 119 | " [3, 4], \n", 120 | " [5, 6]])\n", 121 | "print(x)" 122 | ], 123 | "execution_count": 0, 124 | "outputs": [] 125 | }, 126 | { 127 | "metadata": { 128 | "id": "wKub-KJLcSDJ", 129 | "colab_type": "text" 130 | }, 131 | "cell_type": "markdown", 132 | "source": [ 133 | "## Slicing tensors" 134 | ] 135 | }, 136 | { 137 | "metadata": { 138 | "id": "UxSlfSVrbH8h", 139 | "colab_type": "code", 140 | "colab": {} 141 | }, 142 | "cell_type": "code", 143 | "source": [ 144 | "print(x.size())\n", 145 | "print(x[:, 1]) \n", 146 | "print(x[0, :]) " 147 | ], 148 | "execution_count": 0, 149 | "outputs": [] 150 | }, 151 | { 152 | "metadata": { 153 | "id": "AGWkj2utcrz9", 154 | "colab_type": "code", 155 | "colab": {} 156 | }, 157 | "cell_type": "code", 158 | "source": [ 159 | "y = x[1, 1]\n", 160 | "print(y)\n", 161 | "print(y.item())" 162 | ], 163 | "execution_count": 0, 164 | "outputs": [] 165 | }, 166 | { 167 | "metadata": { 168 | "id": "6YvWGrX0cUpf", 169 | "colab_type": "text" 170 | }, 171 | "cell_type": "markdown", 172 | "source": [ 173 | "## Reshaping tensors" 174 | ] 175 | }, 176 | { 177 | "metadata": { 178 | "id": "mn1q-Hm7b6hP", 179 | "colab_type": "code", 180 | "colab": {} 181 | }, 182 | "cell_type": "code", 183 | "source": [ 184 | "print(x)\n", 185 | "y = x.view(2, 3)\n", 186 | "print(y)" 187 | ], 188 | "execution_count": 0, 189 | "outputs": [] 190 | }, 191 | { 192 | "metadata": { 193 | "id": "1EbIwPvBF4Lg", 194 | "colab_type": "code", 195 | "colab": {} 196 | }, 197 | "cell_type": "code", 198 | "source": [ 199 | "y = x.view(6,-1) \n", 200 | "print(y)" 201 | ], 202 | "execution_count": 0, 203 | "outputs": [] 204 | }, 205 | { 206 | "metadata": { 207 | "id": "2XxOq0ObdXEC", 208 | "colab_type": "text" 209 | }, 210 | "cell_type": "markdown", 211 | "source": [ 212 | "## Simple Tensor Operations" 213 | ] 214 | }, 215 | { 216 | "metadata": { 217 | "id": "Rv4jjqBVdIB2", 218 | "colab_type": "code", 219 | "colab": {} 220 | }, 221 | "cell_type": "code", 222 | "source": [ 223 | "x = torch.ones([3, 2])\n", 224 | "y = torch.ones([3, 2])\n", 225 | "z = x + y\n", 226 | "print(z)\n", 227 | "z = x - y\n", 228 | "print(z)\n", 229 | "z = x * y\n", 230 | "print(z)" 231 | ], 232 | "execution_count": 0, 233 | "outputs": [] 234 | }, 235 | { 236 | "metadata": { 237 | "id": "dVHnXB78dl8s", 238 | "colab_type": "code", 239 | "colab": {} 240 | }, 241 | "cell_type": "code", 242 | "source": [ 243 | "z = y.add(x)\n", 244 | "print(z)\n", 245 | "print(y)" 246 | ], 247 | "execution_count": 0, 248 | "outputs": [] 249 | }, 250 | { 251 | "metadata": { 252 | "id": "LewBBuz_eL1m", 253 | "colab_type": "code", 254 | "colab": {} 255 | }, 256 | "cell_type": "code", 257 | "source": [ 258 | "z = y.add_(x)\n", 259 | "print(z)\n", 260 | "print(y)" 261 | ], 262 | "execution_count": 0, 263 | "outputs": [] 264 | }, 265 | { 266 | "metadata": { 267 | "id": "PDuBSdzTc2Bq", 268 | "colab_type": "text" 269 | }, 270 | "cell_type": "markdown", 271 | "source": [ 272 | "## Numpy <> PyTorch" 273 | ] 274 | }, 275 | { 276 | "metadata": { 277 | "id": "NlvqO8_1ccML", 278 | "colab_type": "code", 279 | "colab": {} 280 | }, 281 | "cell_type": "code", 282 | "source": [ 283 | "x_np = x.numpy()\n", 284 | "print(type(x), type(x_np))\n", 285 | "print(x_np)" 286 | ], 287 | "execution_count": 0, 288 | "outputs": [] 289 | }, 290 | { 291 | "metadata": { 292 | "id": "tLhS3Hrmc-M2", 293 | "colab_type": "code", 294 | "colab": {} 295 | }, 296 | "cell_type": "code", 297 | "source": [ 298 | "a = np.random.randn(5)\n", 299 | "print(a)\n", 300 | "a_pt = torch.from_numpy(a)\n", 301 | "print(type(a), type(a_pt))\n", 302 | "print(a_pt)" 303 | ], 304 | "execution_count": 0, 305 | "outputs": [] 306 | }, 307 | { 308 | "metadata": { 309 | "id": "kwZhRYVtdp-X", 310 | "colab_type": "code", 311 | "colab": {} 312 | }, 313 | "cell_type": "code", 314 | "source": [ 315 | "np.add(a, 1, out=a)\n", 316 | "print(a)\n", 317 | "print(a_pt) " 318 | ], 319 | "execution_count": 0, 320 | "outputs": [] 321 | }, 322 | { 323 | "metadata": { 324 | "id": "6z-Mhf2hewcU", 325 | "colab_type": "code", 326 | "colab": {} 327 | }, 328 | "cell_type": "code", 329 | "source": [ 330 | "%%time\n", 331 | "for i in range(100):\n", 332 | " a = np.random.randn(100,100)\n", 333 | " b = np.random.randn(100,100)\n", 334 | " c = np.matmul(a, b)" 335 | ], 336 | "execution_count": 0, 337 | "outputs": [] 338 | }, 339 | { 340 | "metadata": { 341 | "id": "aFzIX2qge3x9", 342 | "colab_type": "code", 343 | "colab": {} 344 | }, 345 | "cell_type": "code", 346 | "source": [ 347 | "%%time\n", 348 | "for i in range(100):\n", 349 | " a = torch.randn([100, 100])\n", 350 | " b = torch.randn([100, 100])\n", 351 | " c = torch.matmul(a, b)" 352 | ], 353 | "execution_count": 0, 354 | "outputs": [] 355 | }, 356 | { 357 | "metadata": { 358 | "id": "Pdat0Hnm6hGA", 359 | "colab_type": "code", 360 | "colab": {} 361 | }, 362 | "cell_type": "code", 363 | "source": [ 364 | "%%time\n", 365 | "for i in range(10):\n", 366 | " a = np.random.randn(10000,10000)\n", 367 | " b = np.random.randn(10000,10000)\n", 368 | " c = a + b" 369 | ], 370 | "execution_count": 0, 371 | "outputs": [] 372 | }, 373 | { 374 | "metadata": { 375 | "id": "XlRx5OKl6kEq", 376 | "colab_type": "code", 377 | "colab": {} 378 | }, 379 | "cell_type": "code", 380 | "source": [ 381 | "%%time\n", 382 | "for i in range(10):\n", 383 | " a = torch.randn([10000, 10000])\n", 384 | " b = torch.randn([10000, 10000])\n", 385 | " c = a + b" 386 | ], 387 | "execution_count": 0, 388 | "outputs": [] 389 | }, 390 | { 391 | "metadata": { 392 | "id": "de5YwtfUgMWO", 393 | "colab_type": "text" 394 | }, 395 | "cell_type": "markdown", 396 | "source": [ 397 | "## CUDA support" 398 | ] 399 | }, 400 | { 401 | "metadata": { 402 | "id": "-nI4nYcWgY1B", 403 | "colab_type": "code", 404 | "colab": {} 405 | }, 406 | "cell_type": "code", 407 | "source": [ 408 | "print(torch.cuda.device_count())" 409 | ], 410 | "execution_count": 0, 411 | "outputs": [] 412 | }, 413 | { 414 | "metadata": { 415 | "id": "_3E-PMC1gfKU", 416 | "colab_type": "code", 417 | "colab": {} 418 | }, 419 | "cell_type": "code", 420 | "source": [ 421 | "print(torch.cuda.device(0))\n", 422 | "print(torch.cuda.get_device_name(0))" 423 | ], 424 | "execution_count": 0, 425 | "outputs": [] 426 | }, 427 | { 428 | "metadata": { 429 | "id": "_eZYxVpMgor4", 430 | "colab_type": "code", 431 | "colab": {} 432 | }, 433 | "cell_type": "code", 434 | "source": [ 435 | "cuda0 = torch.device('cuda:0')" 436 | ], 437 | "execution_count": 0, 438 | "outputs": [] 439 | }, 440 | { 441 | "metadata": { 442 | "id": "j1r7y57x9JZU", 443 | "colab_type": "code", 444 | "colab": {} 445 | }, 446 | "cell_type": "code", 447 | "source": [ 448 | "a = torch.ones(3, 2, device=cuda0)\n", 449 | "b = torch.ones(3, 2, device=cuda0)\n", 450 | "c = a + b\n", 451 | "print(c)" 452 | ], 453 | "execution_count": 0, 454 | "outputs": [] 455 | }, 456 | { 457 | "metadata": { 458 | "id": "wSt3W1s-_Gc3", 459 | "colab_type": "code", 460 | "colab": {} 461 | }, 462 | "cell_type": "code", 463 | "source": [ 464 | "print(a)" 465 | ], 466 | "execution_count": 0, 467 | "outputs": [] 468 | }, 469 | { 470 | "metadata": { 471 | "id": "sVfOAU2EfTXB", 472 | "colab_type": "code", 473 | "colab": {} 474 | }, 475 | "cell_type": "code", 476 | "source": [ 477 | "%%time\n", 478 | "for i in range(10):\n", 479 | " a = np.random.randn(10000,10000)\n", 480 | " b = np.random.randn(10000,10000)\n", 481 | " np.add(b, a)" 482 | ], 483 | "execution_count": 0, 484 | "outputs": [] 485 | }, 486 | { 487 | "metadata": { 488 | "id": "G9GLUek5hCfn", 489 | "colab_type": "code", 490 | "colab": {} 491 | }, 492 | "cell_type": "code", 493 | "source": [ 494 | "%%time\n", 495 | "for i in range(10):\n", 496 | " a_cpu = torch.randn([10000, 10000])\n", 497 | " b_cpu = torch.randn([10000, 10000])\n", 498 | " b_cpu.add_(a_cpu)" 499 | ], 500 | "execution_count": 0, 501 | "outputs": [] 502 | }, 503 | { 504 | "metadata": { 505 | "id": "FqSYioGrgyMI", 506 | "colab_type": "code", 507 | "colab": {} 508 | }, 509 | "cell_type": "code", 510 | "source": [ 511 | "%%time\n", 512 | "for i in range(10):\n", 513 | " a = torch.randn([10000, 10000], device=cuda0)\n", 514 | " b = torch.randn([10000, 10000], device=cuda0)\n", 515 | " b.add_(a)" 516 | ], 517 | "execution_count": 0, 518 | "outputs": [] 519 | }, 520 | { 521 | "metadata": { 522 | "id": "Kjsl8xRFjPtT", 523 | "colab_type": "code", 524 | "colab": {} 525 | }, 526 | "cell_type": "code", 527 | "source": [ 528 | "%%time\n", 529 | "for i in range(10):\n", 530 | " a = np.random.randn(10000,10000)\n", 531 | " b = np.random.randn(10000,10000)\n", 532 | " np.matmul(b, a)" 533 | ], 534 | "execution_count": 0, 535 | "outputs": [] 536 | }, 537 | { 538 | "metadata": { 539 | "id": "avFqbCgXjT3F", 540 | "colab_type": "code", 541 | "colab": {} 542 | }, 543 | "cell_type": "code", 544 | "source": [ 545 | "%%time\n", 546 | "for i in range(10):\n", 547 | " a_cpu = torch.randn([10000, 10000])\n", 548 | " b_cpu = torch.randn([10000, 10000])\n", 549 | " torch.matmul(a_cpu, b_cpu)" 550 | ], 551 | "execution_count": 0, 552 | "outputs": [] 553 | }, 554 | { 555 | "metadata": { 556 | "id": "hFfMhN2gjlZJ", 557 | "colab_type": "code", 558 | "colab": {} 559 | }, 560 | "cell_type": "code", 561 | "source": [ 562 | "%%time\n", 563 | "for i in range(10):\n", 564 | " a = torch.randn([10000, 10000], device=cuda0)\n", 565 | " b = torch.randn([10000, 10000], device=cuda0)\n", 566 | " torch.matmul(a, b)" 567 | ], 568 | "execution_count": 0, 569 | "outputs": [] 570 | }, 571 | { 572 | "metadata": { 573 | "id": "P_6TU64Gi7jv", 574 | "colab_type": "text" 575 | }, 576 | "cell_type": "markdown", 577 | "source": [ 578 | "## Autodiff" 579 | ] 580 | }, 581 | { 582 | "metadata": { 583 | "id": "PjySsLMThEX7", 584 | "colab_type": "code", 585 | "outputId": "b1cef456-8281-42b0-8205-d7b988c3c383", 586 | "colab": { 587 | "base_uri": "https://localhost:8080/", 588 | "height": 74 589 | } 590 | }, 591 | "cell_type": "code", 592 | "source": [ 593 | "x = torch.ones([3, 2], requires_grad=True)\n", 594 | "print(x)" 595 | ], 596 | "execution_count": 2, 597 | "outputs": [ 598 | { 599 | "output_type": "stream", 600 | "text": [ 601 | "tensor([[1., 1.],\n", 602 | " [1., 1.],\n", 603 | " [1., 1.]], requires_grad=True)\n" 604 | ], 605 | "name": "stdout" 606 | } 607 | ] 608 | }, 609 | { 610 | "metadata": { 611 | "id": "neb3oFWBjAtJ", 612 | "colab_type": "code", 613 | "outputId": "a9e1faf0-841d-41d1-f9c4-e429e1fdd0bc", 614 | "colab": { 615 | "base_uri": "https://localhost:8080/", 616 | "height": 74 617 | } 618 | }, 619 | "cell_type": "code", 620 | "source": [ 621 | "y = x + 5\n", 622 | "print(y)" 623 | ], 624 | "execution_count": 3, 625 | "outputs": [ 626 | { 627 | "output_type": "stream", 628 | "text": [ 629 | "tensor([[6., 6.],\n", 630 | " [6., 6.],\n", 631 | " [6., 6.]], grad_fn=)\n" 632 | ], 633 | "name": "stdout" 634 | } 635 | ] 636 | }, 637 | { 638 | "metadata": { 639 | "id": "5M0pnstAjLa-", 640 | "colab_type": "code", 641 | "outputId": "b9861325-9ec6-41bb-995f-ae54210628c2", 642 | "colab": { 643 | "base_uri": "https://localhost:8080/", 644 | "height": 74 645 | } 646 | }, 647 | "cell_type": "code", 648 | "source": [ 649 | "z = y*y + 1\n", 650 | "print(z)" 651 | ], 652 | "execution_count": 4, 653 | "outputs": [ 654 | { 655 | "output_type": "stream", 656 | "text": [ 657 | "tensor([[37., 37.],\n", 658 | " [37., 37.],\n", 659 | " [37., 37.]], grad_fn=)\n" 660 | ], 661 | "name": "stdout" 662 | } 663 | ] 664 | }, 665 | { 666 | "metadata": { 667 | "id": "wHHDSmiUkMOw", 668 | "colab_type": "code", 669 | "outputId": "93374258-d506-4eba-c359-13fc160d0123", 670 | "colab": { 671 | "base_uri": "https://localhost:8080/", 672 | "height": 36 673 | } 674 | }, 675 | "cell_type": "code", 676 | "source": [ 677 | "t = torch.sum(z)\n", 678 | "print(t)" 679 | ], 680 | "execution_count": 5, 681 | "outputs": [ 682 | { 683 | "output_type": "stream", 684 | "text": [ 685 | "tensor(222., grad_fn=)\n" 686 | ], 687 | "name": "stdout" 688 | } 689 | ] 690 | }, 691 | { 692 | "metadata": { 693 | "id": "AXj896azkM_S", 694 | "colab_type": "code", 695 | "colab": {} 696 | }, 697 | "cell_type": "code", 698 | "source": [ 699 | "t.backward()" 700 | ], 701 | "execution_count": 0, 702 | "outputs": [] 703 | }, 704 | { 705 | "metadata": { 706 | "id": "wSYAcNN1lAWS", 707 | "colab_type": "code", 708 | "outputId": "c82aa737-eb40-45f5-ad44-5e28484d5e72", 709 | "colab": { 710 | "base_uri": "https://localhost:8080/", 711 | "height": 74 712 | } 713 | }, 714 | "cell_type": "code", 715 | "source": [ 716 | "print(x.grad)" 717 | ], 718 | "execution_count": 7, 719 | "outputs": [ 720 | { 721 | "output_type": "stream", 722 | "text": [ 723 | "tensor([[12., 12.],\n", 724 | " [12., 12.],\n", 725 | " [12., 12.]])\n" 726 | ], 727 | "name": "stdout" 728 | } 729 | ] 730 | }, 731 | { 732 | "metadata": { 733 | "id": "6nrD44oJiEIY", 734 | "colab_type": "text" 735 | }, 736 | "cell_type": "markdown", 737 | "source": [ 738 | "$t = \\sum_i z_i, z_i = y_i^2 + 1, y_i = x_i + 5$\n", 739 | "\n", 740 | "$\\frac{\\partial t}{\\partial x_i} = \\frac{\\partial z_i}{\\partial x_i} = \\frac{\\partial z_i}{\\partial y_i} \\frac{\\partial y_i}{\\partial x_i} = 2y_i \\times 1$\n", 741 | "\n", 742 | "\n", 743 | "At x = 1, y = 6, $\\frac{\\partial t}{\\partial x_i} = 12$" 744 | ] 745 | }, 746 | { 747 | "metadata": { 748 | "id": "ZFCWPPAP6ipv", 749 | "colab_type": "code", 750 | "colab": { 751 | "base_uri": "https://localhost:8080/", 752 | "height": 132 753 | }, 754 | "outputId": "d1d2243a-af30-4317-e303-3eee8058400e" 755 | }, 756 | "cell_type": "code", 757 | "source": [ 758 | "x = torch.ones([3, 2], requires_grad=True)\n", 759 | "y = x + 5\n", 760 | "r = 1/(1 + torch.exp(-y))\n", 761 | "print(r)\n", 762 | "s = torch.sum(r)\n", 763 | "s.backward()\n", 764 | "print(x.grad)" 765 | ], 766 | "execution_count": 8, 767 | "outputs": [ 768 | { 769 | "output_type": "stream", 770 | "text": [ 771 | "tensor([[0.9975, 0.9975],\n", 772 | " [0.9975, 0.9975],\n", 773 | " [0.9975, 0.9975]], grad_fn=)\n", 774 | "tensor([[0.0025, 0.0025],\n", 775 | " [0.0025, 0.0025],\n", 776 | " [0.0025, 0.0025]])\n" 777 | ], 778 | "name": "stdout" 779 | } 780 | ] 781 | }, 782 | { 783 | "metadata": { 784 | "id": "Ts1wsONqlE5h", 785 | "colab_type": "code", 786 | "outputId": "f42bd6a2-0c97-4b44-96d0-3e9eae2b90a0", 787 | "colab": { 788 | "base_uri": "https://localhost:8080/", 789 | "height": 74 790 | } 791 | }, 792 | "cell_type": "code", 793 | "source": [ 794 | "x = torch.ones([3, 2], requires_grad=True)\n", 795 | "y = x + 5\n", 796 | "r = 1/(1 + torch.exp(-y))\n", 797 | "a = torch.ones([3, 2])\n", 798 | "r.backward(a)\n", 799 | "print(x.grad)" 800 | ], 801 | "execution_count": 9, 802 | "outputs": [ 803 | { 804 | "output_type": "stream", 805 | "text": [ 806 | "tensor([[0.0025, 0.0025],\n", 807 | " [0.0025, 0.0025],\n", 808 | " [0.0025, 0.0025]])\n" 809 | ], 810 | "name": "stdout" 811 | } 812 | ] 813 | }, 814 | { 815 | "metadata": { 816 | "id": "56AqY5hY77dx", 817 | "colab_type": "text" 818 | }, 819 | "cell_type": "markdown", 820 | "source": [ 821 | "$\\frac{\\partial{s}}{\\partial{x}} = \\frac{\\partial{s}}{\\partial{r}} \\cdot \\frac{\\partial{r}}{\\partial{x}}$\n", 822 | "\n", 823 | "For the above code $a$ represents $\\frac{\\partial{s}}{\\partial{r}}$ and then $x.grad$ gives directly $\\frac{\\partial{s}}{\\partial{x}}$\n", 824 | "\n" 825 | ] 826 | }, 827 | { 828 | "metadata": { 829 | "id": "AKhxwdYUpUfj", 830 | "colab_type": "text" 831 | }, 832 | "cell_type": "markdown", 833 | "source": [ 834 | "## Autodiff example that looks like what we have been doing" 835 | ] 836 | }, 837 | { 838 | "metadata": { 839 | "id": "THNkQLR6mmpO", 840 | "colab_type": "code", 841 | "colab": {} 842 | }, 843 | "cell_type": "code", 844 | "source": [ 845 | "x = torch.randn([20, 1], requires_grad=True)\n", 846 | "y = 3*x - 2" 847 | ], 848 | "execution_count": 0, 849 | "outputs": [] 850 | }, 851 | { 852 | "metadata": { 853 | "id": "-t4_8qgdnjDk", 854 | "colab_type": "code", 855 | "colab": {} 856 | }, 857 | "cell_type": "code", 858 | "source": [ 859 | "w = torch.tensor([1.], requires_grad=True)\n", 860 | "b = torch.tensor([1.], requires_grad=True)\n", 861 | "\n", 862 | "y_hat = w*x + b\n", 863 | "\n", 864 | "loss = torch.sum((y_hat - y)**2)" 865 | ], 866 | "execution_count": 0, 867 | "outputs": [] 868 | }, 869 | { 870 | "metadata": { 871 | "id": "Gvpc37u-o6ob", 872 | "colab_type": "code", 873 | "outputId": "6846154f-587d-4aa3-cad5-4e3be6d08fc2", 874 | "colab": { 875 | "base_uri": "https://localhost:8080/", 876 | "height": 36 877 | } 878 | }, 879 | "cell_type": "code", 880 | "source": [ 881 | "print(loss)" 882 | ], 883 | "execution_count": 14, 884 | "outputs": [ 885 | { 886 | "output_type": "stream", 887 | "text": [ 888 | "tensor(318.2823, grad_fn=)\n" 889 | ], 890 | "name": "stdout" 891 | } 892 | ] 893 | }, 894 | { 895 | "metadata": { 896 | "id": "-tnKq6DXo-RB", 897 | "colab_type": "code", 898 | "colab": {} 899 | }, 900 | "cell_type": "code", 901 | "source": [ 902 | "loss.backward()" 903 | ], 904 | "execution_count": 0, 905 | "outputs": [] 906 | }, 907 | { 908 | "metadata": { 909 | "id": "I38qmZLhpM2F", 910 | "colab_type": "code", 911 | "outputId": "9ab231ff-c34e-450f-9d3a-8ef2b8068096", 912 | "colab": { 913 | "base_uri": "https://localhost:8080/", 914 | "height": 36 915 | } 916 | }, 917 | "cell_type": "code", 918 | "source": [ 919 | "print(w.grad, b.grad)" 920 | ], 921 | "execution_count": 16, 922 | "outputs": [ 923 | { 924 | "output_type": "stream", 925 | "text": [ 926 | "tensor([-106.4956]) tensor([141.1912])\n" 927 | ], 928 | "name": "stdout" 929 | } 930 | ] 931 | }, 932 | { 933 | "metadata": { 934 | "id": "WfDV6saTq8XA", 935 | "colab_type": "text" 936 | }, 937 | "cell_type": "markdown", 938 | "source": [ 939 | "## Do it in a loop" 940 | ] 941 | }, 942 | { 943 | "metadata": { 944 | "id": "ivmJgJQTpN79", 945 | "colab_type": "code", 946 | "outputId": "831ec258-0f1e-4dbb-843f-d413a54c0784", 947 | "colab": { 948 | "base_uri": "https://localhost:8080/", 949 | "height": 227 950 | } 951 | }, 952 | "cell_type": "code", 953 | "source": [ 954 | "learning_rate = 0.01\n", 955 | "\n", 956 | "w = torch.tensor([1.], requires_grad=True)\n", 957 | "b = torch.tensor([1.], requires_grad=True)\n", 958 | "\n", 959 | "print(w.item(), b.item())\n", 960 | "\n", 961 | "for i in range(10):\n", 962 | " \n", 963 | " x = torch.randn([20, 1])\n", 964 | " y = 3*x - 2\n", 965 | " \n", 966 | " y_hat = w*x + b\n", 967 | " loss = torch.sum((y_hat - y)**2)\n", 968 | " \n", 969 | " loss.backward()\n", 970 | " \n", 971 | " with torch.no_grad():\n", 972 | " w -= learning_rate * w.grad\n", 973 | " b -= learning_rate * b.grad\n", 974 | " \n", 975 | " w.grad.zero_()\n", 976 | " b.grad.zero_()\n", 977 | "\n", 978 | " print(w.item(), b.item())\n", 979 | " " 980 | ], 981 | "execution_count": 17, 982 | "outputs": [ 983 | { 984 | "output_type": "stream", 985 | "text": [ 986 | "1.0 1.0\n", 987 | "1.694516658782959 -0.32816600799560547\n", 988 | "2.5244972705841064 -0.9011859893798828\n", 989 | "2.6990771293640137 -1.3381099700927734\n", 990 | "2.7810328006744385 -1.5905817747116089\n", 991 | "2.821857213973999 -1.7378290891647339\n", 992 | "2.943121910095215 -1.868725061416626\n", 993 | "2.9525837898254395 -1.9191371202468872\n", 994 | "2.9741718769073486 -1.9551563262939453\n", 995 | "2.9911296367645264 -1.972025752067566\n", 996 | "2.994936943054199 -1.9838125705718994\n" 997 | ], 998 | "name": "stdout" 999 | } 1000 | ] 1001 | }, 1002 | { 1003 | "metadata": { 1004 | "id": "vyOqrZZiuLkl", 1005 | "colab_type": "text" 1006 | }, 1007 | "cell_type": "markdown", 1008 | "source": [ 1009 | "## Do it for a large problem" 1010 | ] 1011 | }, 1012 | { 1013 | "metadata": { 1014 | "id": "qq3Iykk1rMfh", 1015 | "colab_type": "code", 1016 | "outputId": "cd799469-ac56-4328-aa9e-33e0c17e57c6", 1017 | "colab": { 1018 | "base_uri": "https://localhost:8080/", 1019 | "height": 55 1020 | } 1021 | }, 1022 | "cell_type": "code", 1023 | "source": [ 1024 | "%%time\n", 1025 | "learning_rate = 0.001\n", 1026 | "N = 10000000\n", 1027 | "epochs = 200\n", 1028 | "\n", 1029 | "w = torch.rand([N], requires_grad=True)\n", 1030 | "b = torch.ones([1], requires_grad=True)\n", 1031 | "\n", 1032 | "# print(torch.mean(w).item(), b.item())\n", 1033 | "\n", 1034 | "for i in range(epochs):\n", 1035 | " \n", 1036 | " x = torch.randn([N])\n", 1037 | " y = torch.dot(3*torch.ones([N]), x) - 2\n", 1038 | " \n", 1039 | " y_hat = torch.dot(w, x) + b\n", 1040 | " loss = torch.sum((y_hat - y)**2)\n", 1041 | " \n", 1042 | " loss.backward()\n", 1043 | " \n", 1044 | " with torch.no_grad():\n", 1045 | " w -= learning_rate * w.grad\n", 1046 | " b -= learning_rate * b.grad\n", 1047 | " \n", 1048 | " w.grad.zero_()\n", 1049 | " b.grad.zero_()\n", 1050 | "\n", 1051 | "# print(torch.mean(w).item(), b.item())\n", 1052 | " " 1053 | ], 1054 | "execution_count": 30, 1055 | "outputs": [ 1056 | { 1057 | "output_type": "stream", 1058 | "text": [ 1059 | "CPU times: user 36.7 s, sys: 443 ms, total: 37.2 s\n", 1060 | "Wall time: 37.2 s\n" 1061 | ], 1062 | "name": "stdout" 1063 | } 1064 | ] 1065 | }, 1066 | { 1067 | "metadata": { 1068 | "id": "owaeEn4A01zF", 1069 | "colab_type": "code", 1070 | "colab": { 1071 | "base_uri": "https://localhost:8080/", 1072 | "height": 55 1073 | }, 1074 | "outputId": "1dba52f0-f0de-41b8-8325-ab1e1a6e21b3" 1075 | }, 1076 | "cell_type": "code", 1077 | "source": [ 1078 | "%%time\n", 1079 | "learning_rate = 0.001\n", 1080 | "N = 10000000\n", 1081 | "epochs = 200\n", 1082 | "\n", 1083 | "w = torch.rand([N], requires_grad=True, device=cuda0)\n", 1084 | "b = torch.ones([1], requires_grad=True, device=cuda0)\n", 1085 | "\n", 1086 | "# print(torch.mean(w).item(), b.item())\n", 1087 | "\n", 1088 | "for i in range(epochs):\n", 1089 | " \n", 1090 | " x = torch.randn([N], device=cuda0)\n", 1091 | " y = torch.dot(3*torch.ones([N], device=cuda0), x) - 2\n", 1092 | " \n", 1093 | " y_hat = torch.dot(w, x) + b\n", 1094 | " loss = torch.sum((y_hat - y)**2)\n", 1095 | " \n", 1096 | " loss.backward()\n", 1097 | " \n", 1098 | " with torch.no_grad():\n", 1099 | " w -= learning_rate * w.grad\n", 1100 | " b -= learning_rate * b.grad\n", 1101 | " \n", 1102 | " w.grad.zero_()\n", 1103 | " b.grad.zero_()\n", 1104 | "\n", 1105 | " #print(torch.mean(w).item(), b.item())\n", 1106 | " " 1107 | ], 1108 | "execution_count": 31, 1109 | "outputs": [ 1110 | { 1111 | "output_type": "stream", 1112 | "text": [ 1113 | "CPU times: user 467 ms, sys: 305 ms, total: 772 ms\n", 1114 | "Wall time: 784 ms\n" 1115 | ], 1116 | "name": "stdout" 1117 | } 1118 | ] 1119 | }, 1120 | { 1121 | "metadata": { 1122 | "id": "4TmBA3VO9Jva", 1123 | "colab_type": "code", 1124 | "colab": {} 1125 | }, 1126 | "cell_type": "code", 1127 | "source": [ 1128 | "" 1129 | ], 1130 | "execution_count": 0, 1131 | "outputs": [] 1132 | } 1133 | ] 1134 | } -------------------------------------------------------------------------------- /PadhAI-Download-Materials/FFNetworkMultiClass-1553058835337.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/satyajitghana/PadhAI-Course/fd72c0e359b2d0599d52210b33a692a4210c4c1b/PadhAI-Download-Materials/FFNetworkMultiClass-1553058835337.png 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0,12,55,155.0,250,1.3,10.5,5,2.0,0,4.5,15,3,0,0,132.9,26,3,3870,9,480,12,3,11,5.0,1,1,7,29,6,4,2,3,67.8,64.0,5,3,2000,125,1 3 | 0,1,55,132.0,300,1.3,10.6,5,0.3,1,4.0,30,2,6,0,124.5,26,5,4059,9,720,15,3,11,5.0,1,1,7,11,6,4,4,6,64.0,32.0,5,3,2000,165,1 4 | 0,9,55,142.0,329,1.5,8.5,5,2.0,3,5.0,30,2,20,0,145.5,4,3,4777,10,1080,4,3,1,5.04,2,1,7,27,6,4,9,6,72.0,32.0,6,3,2500,164,0 5 | 0,8,55,152.0,385,1.3,8.0,5,2.0,3,5.0,15,3,0,0,147.5,26,3,5799,19,720,17,3,2,5.0,1,1,7,4,6,4,1,3,75.1,32.0,6,3,3000,165,1 6 | 1,1,55,234.0,385,1.3,7.9,5,1.92,3,5.0,15,3,0,0,179.0,18,3,5990,11,720,17,3,1,5.0,1,1,7,4,6,4,1,6,91.0,32.0,6,3,3000,165,0 7 | 0,14,55,179.0,280,1.3,7.9,5,5.0,3,5.5,30,3,6,0,150.0,5,3,5999,22,720,0,0,2,8.0,1,1,7,4,6,4,7,3,71.0,32.0,6,3,2900,165,0 8 | 1,1,56,124.0,230,1.3,8.8,5,2.0,3,4.0,30,3,0,0,123.0,26,3,5999,11,1080,16,3,2,5.0,1,512,5,30,0,4,4,3,62.5,128.0,2,3,1700,163,1 9 | 0,8,41,154.0,182,1.0,8.1,5,2.0,3,5.0,30,5,0,0,132.0,26,3,6599,7,720,4,3,2,8.0,0,2,2,4,6,4,9,3,78.0,32.0,6,3,2000,92,1 10 | 1,8,41,214.0,182,1.0,8.2,5,2.0,3,5.0,30,3,39,0,172.0,18,3,6599,6,720,14,3,2,8.0,1,2,2,4,6,4,9,6,80.0,32.0,6,7,2000,92,1 11 | 0,1,55,155.0,435,1.3,11.6,5,2.0,3,5.0,15,2,5,0,142.1,5,3,6649,9,720,4,1,2,8.0,1,2,7,4,6,4,9,6,72.4,32.0,5,3,3000,125,1 12 | 0,1,55,169.0,514,1.3,7.9,2,2.0,3,5.0,30,3,5,0,152.0,26,3,6749,14,720,4,1,2,8.0,2,2,7,4,6,4,9,3,75.0,32.0,5,4,4000,126,1 13 | 0,10,55,137.0,300,1.3,7.9,5,5.0,3,5.0,30,2,42,1,141.6,18,3,6990,15,720,3,3,7,8.0,1,1,7,2,6,3,9,6,70.0,128.0,0,3,2600,166,0 14 | 0,1,50,135.0,280,1.4,8.0,7,5.0,3,5.3,30,3,10,0,148.0,26,3,6999,8,1080,4,3,2,13.0,1,1,7,20,4,4,8,2,74.0,32.0,4,3,2500,79,1 15 | 0,11,43,133.0,198,1.2,9.0,5,2.0,3,4.7,30,2,139,0,134.0,5,3,6999,7,1080,14,3,2,8.0,2,2,2,6,6,4,11,6,67.0,32.0,4,8,2200,94,1 16 | 0,8,41,142.0,200,1.3,8.9,5,5.0,3,5.0,30,3,94,0,140.8,21,3,6999,10,1080,4,1,2,13.0,1,3,2,18,6,6,9,6,70.4,32.0,6,3,2500,92,1 17 | 1,10,41,131.0,680,1.3,8.7,5,5.0,3,5.0,30,1,10,0,140.1,1,11,6999,23,1080,14,1,1,13.0,1,2,2,30,6,2,9,3,68.9,256.0,6,3,2500,92,1 18 | 1,10,47,152.0,576,1.2,8.6,5,5.0,3,5.0,30,3,28,3,144.0,26,3,7340,26,480,4,3,2,8.0,1,1,7,7,6,4,9,3,73.7,128.0,13,3,2200,132,1 19 | 0,10,43,128.0,264,1.2,8.2,5,2.0,3,5.0,30,2,126,0,141.0,26,3,7499,13,720,4,3,1,8.0,2,2,2,11,6,4,9,6,70.0,32.0,4,3,2300,94,1 20 | 0,10,41,130.0,180,1.3,8.4,5,2.0,3,4.5,30,3,0,0,69.0,18,3,7590,11,1080,4,3,7,5.0,1,1,7,2,6,6,5,6,136.5,128.0,6,7,2000,100,1 21 | 0,10,43,143.0,160,1.2,8.2,5,5.0,3,5.0,30,3,142,0,142.4,6,3,7790,7,1080,4,3,2,8.0,1,2,2,17,6,6,9,3,73.0,32.0,5,3,2230,94,1 22 | 0,1,55,152.0,450,1.2,9.4,2,5.0,3,5.0,30,2,0,0,143.0,5,3,7899,14,1080,4,1,2,8.0,1,2,7,4,6,4,9,6,71.8,32.0,6,3,4000,94,1 23 | 0,10,34,140.0,264,1.5,8.0,5,5.0,3,5.5,30,5,184,0,152.6,26,3,7914,39,1080,4,3,2,8.0,2,2,7,11,4,4,7,3,76.2,32.0,5,3,2900,16,1 24 | 2,1,38,125.0,354,1.3,7.6,5,5.0,3,5.0,30,5,10,0,144.6,14,11,7999,12,1080,9,1,2,13.0,2,3,4,27,4,2,23,6,72.0,128.0,6,5,2400,19,0 25 | 3,1,43,97.0,345,1.2,5.1,5,5.0,3,4.8,30,3,0,0,141.9,5,9,8490,8,1080,14,1,0,8.0,2,2,2,4,6,2,10,2,68.1,0.0,5,3,2000,94,1 26 | 0,10,29,150.0,322,1.5,8.2,5,5.0,3,5.0,30,3,113,0,142.0,2,12,8499,15,1080,9,3,2,13.0,2,2,2,11,4,6,23,6,71.0,32.0,6,4,2750,21,1 27 | 0,8,43,202.0,914,1.2,10.6,2,5.0,3,5.5,30,4,34,0,156.0,18,3,8999,38,1080,4,1,2,13.0,2,2,2,16,6,6,7,6,77.5,64.0,5,4,5000,94,1 28 | 0,8,43,170.0,456,1.2,10.8,2,5.0,3,5.5,30,2,189,0,152.5,7,3,8999,21,1080,9,3,2,13.0,2,2,2,16,6,4,7,6,77.2,128.0,5,3,3000,94,1 29 | 0,8,38,155.0,350,1.3,9.3,5,5.0,3,5.5,30,3,122,0,151.0,18,3,8999,38,1080,4,1,2,13.0,1,2,2,18,4,6,7,6,77.0,64.0,6,4,3000,19,1 30 | 0,1,44,160.0,617,1.7,9.6,5,5.0,3,5.0,30,3,0,0,150.1,26,12,9399,12,1080,4,3,9,8.0,2,1,7,5,4,4,9,3,72.7,32.0,5,3,2100,79,1 31 | 0,10,43,185.0,775,1.2,9.4,5,5.0,3,5.5,30,2,87,0,154.0,26,3,9499,38,1080,4,3,2,13.0,2,2,2,6,6,4,7,6,78.7,32.0,4,3,3100,94,1 32 | 0,8,55,141.0,218,1.2,7.6,5,8.0,3,5.0,30,2,9,0,142.7,26,10,9700,16,1080,18,1,11,8.0,3,1,2,10,6,4,3,6,71.7,128.0,1,3,2420,94,1 33 | 0,8,34,150.0,270,1.7,8.0,2,5.0,3,5.5,30,2,0,0,152.6,19,3,9715,30,1080,1,3,1,13.0,1,2,2,11,4,4,16,6,76.2,32.0,5,3,2900,16,1 34 | 0,8,43,155.0,250,1.4,11.6,2,5.0,3,5.0,30,2,760,0,142.1,5,3,9999,22,1080,4,1,2,13.0,1,2,2,3,6,4,9,7,72.4,32.0,6,3,2470,94,1 35 | 0,10,28,164.0,264,1.4,8.7,2,5.0,3,5.5,30,2,420,0,150.0,26,3,9999,20,1080,9,1,2,16.0,2,2,4,6,2,1,16,6,76.0,0.0,6,3,4050,12,1 36 | 0,10,28,164.0,265,1.4,8.7,2,5.0,3,5.5,30,2,420,0,150.0,18,3,9999,20,1080,9,1,2,16.0,2,2,4,6,2,3,16,6,76.0,0.0,6,3,4050,12,1 37 | 0,8,34,150.0,750,1.7,8.0,2,5.0,3,5.5,30,3,608,0,152.6,26,3,9999,36,1080,9,3,2,13.0,1,2,2,11,4,6,16,6,76.2,32.0,5,4,3000,90,1 38 | 0,10,34,138.0,521,1.7,8.2,5,5.0,3,5.0,30,3,20,0,146.9,26,9,9999,23,1080,4,3,2,13.0,2,1,7,5,4,4,9,6,70.9,32.0,4,3,2000,16,1 39 | 1,10,38,149.0,360,1.3,8.7,2,5.0,3,5.5,30,1,0,0,150.9,1,11,9999,20,1080,9,1,2,13.0,6,2,4,30,4,4,18,3,75.2,128.0,5,3,3100,19,1 40 | 2,0,27,128.0,490,1.7,8.8,5,5.0,3,5.0,30,3,0,0,140.8,18,11,9999,12,720,5,2,2,13.0,6,3,2,4,9,4,9,3,70.4,32.0,6,3,2100,19,0 41 | 0,4,41,172.0,350,1.2,8.2,5,5.0,3,5.5,30,5,0,0,151.8,18,3,10190,21,1080,4,3,12,13.0,1,2,7,2,6,6,7,6,77.5,128.0,6,3,3000,94,1 42 | 0,1,38,143.0,275,1.3,8.5,5,5.0,3,5.2,30,3,21,0,148.0,18,3,10899,10,1080,9,1,2,13.0,2,3,2,4,4,5,20,6,73.6,0.0,6,3,2900,90,1 43 | 0,8,28,169.0,496,2.2,7.5,5,5.0,3,5.5,30,1,26,0,151.1,17,2,10999,20,2160,9,1,2,13.0,2,3,4,19,4,1,9,6,74.2,0.0,8,1,3000,14,1 44 | 0,8,34,149.0,696,1.7,8.9,5,5.0,3,5.0,30,3,43,0,142.0,26,3,11090,46,1080,4,1,2,13.0,2,2,2,11,4,4,9,3,71.8,32.0,4,4,4000,16,1 45 | 0,8,41,153.2,261,1.3,8.3,2,8.0,3,5.5,30,3,0,0,151.9,5,3,11299,28,1080,0,3,2,13.0,2,3,2,14,6,4,7,3,76.7,32.0,6,3,3000,92,1 46 | 0,8,29,130.0,384,1.7,7.8,2,5.0,3,5.0,30,3,385,0,138.1,13,0,11999,35,1080,1,1,2,13.0,1,2,2,6,4,2,23,6,69.6,0.0,5,3,3120,22,1 47 | 0,10,38,160.0,265,1.3,9.2,2,5.0,3,5.5,30,2,1016,0,153.6,18,3,11999,22,1080,9,1,2,13.0,2,3,2,11,2,3,17,6,76.5,128.0,6,3,3300,12,1 48 | 0,10,29,155.0,342,1.5,11.6,2,5.0,3,5.0,30,3,0,0,142.1,5,0,12499,28,1080,4,1,12,13.0,1,2,2,3,4,4,34,5,72.4,32.0,6,2,2470,22,1 49 | 0,13,34,138.0,761,1.7,8.2,5,5.0,3,5.0,30,4,2,0,146.9,18,11,13349,30,1080,3,1,2,13.0,1,2,2,5,4,3,9,6,70.9,32.0,4,3,2000,16,1 50 | 0,3,41,148.0,566,1.0,9.3,5,5.0,3,5.0,30,2,0,0,141.0,5,3,13349,16,720,4,1,2,8.0,2,2,2,11,6,3,9,6,71.8,32.0,6,3,4000,92,1 51 | 0,8,29,155.0,620,1.5,9.8,2,5.0,3,5.5,30,3,671,0,153.0,5,3,13499,24,1080,9,4,1,16.0,2,3,3,3,9,4,16,3,76.6,128.0,9,3,3000,72,1 52 | 0,7,29,171.0,354,1.6,7.5,5,5.0,3,5.5,30,2,73,0,152.4,26,3,13990,23,1080,4,3,7,13.0,1,2,0,2,4,3,16,6,78.6,128.0,6,3,3000,72,1 53 | 0,8,28,150.0,337,2.0,8.7,5,5.0,3,5.5,30,2,25,0,156.8,5,4,13999,9,1080,9,1,2,13.0,1,4,2,4,4,3,16,6,78.1,64.0,6,3,3000,14,1 54 | 0,10,30,170.0,354,1.6,7.8,2,5.0,3,5.5,30,2,301,0,151.7,18,3,14249,23,1080,9,3,7,13.0,1,3,2,2,4,3,16,6,76.0,128.0,9,3,3300,72,1 55 | 0,9,43,141.0,345,1.2,7.3,6,5.0,4,5.5,30,5,0,0,151.3,7,11,14300,18,1080,4,4,7,13.0,1,2,2,2,11,4,7,3,77.2,64.0,4,3,2950,94,0 56 | 0,1,44,154.5,560,1.7,7.7,5,8.0,3,5.5,30,2,12,0,157.7,26,9,14614,12,1080,4,1,2,13.0,2,1,2,5,4,4,7,6,78.7,32.0,4,3,2600,79,1 57 | 1,1,44,171.8,1093,1.4,7.7,5,1.1,3,6.0,30,3,24,0,165.2,26,3,14880,30,1080,4,1,11,13.0,1,1,7,8,6,4,6,6,83.8,32.0,3,3,3000,129,1 58 | 1,10,29,229.0,480,1.5,7.6,4,5.0,3,6.8,30,3,0,0,186.6,18,3,14990,24,1080,1,1,1,13.0,2,2,4,11,4,4,12,6,96.6,64.0,5,3,3500,22,1 59 | 0,10,16,160.0,200,2.3,6.9,5,8.0,3,5.0,30,3,175,0,140.0,5,9,14999,24,1080,9,1,0,13.0,2,3,2,12,6,1,23,6,69.0,0.0,6,4,2525,15,1 60 | 3,6,16,149.0,280,2.5,8.9,5,8.0,3,5.0,30,2,33,0,139.2,26,3,14999,18,2160,1,1,2,13.0,1,3,2,6,6,2,23,6,68.5,0.0,4,3,3080,15,1 61 | 3,3,16,144.0,250,2.5,10.0,2,13.0,3,5.2,30,4,188,0,140.8,5,11,14999,24,2160,9,1,5,13.0,1,2,2,3,6,1,20,4,72.4,0.0,4,3,2300,15,1 62 | 2,10,16,175.0,840,2.5,8.9,3,8.0,3,5.5,30,1,6,0,155.7,15,11,15689,48,1080,9,1,4,13.0,2,3,6,11,6,0,16,3,77.3,0.0,6,14,4100,15,0 63 | 0,8,29,134.0,380,1.7,7.3,5,8.0,3,5.0,30,1,0,0,143.5,8,2,15950,20,1080,9,1,2,13.0,1,3,2,10,4,3,9,6,71.0,128.0,6,3,2500,21,1 64 | 0,7,30,170.0,354,1.6,7.8,5,5.0,3,5.5,30,3,301,0,151.7,26,3,15990,23,1080,0,3,7,13.0,1,3,2,2,4,4,7,6,76.0,32.0,9,3,3300,72,1 65 | 0,13,29,189.0,600,1.5,9.9,2,5.0,3,5.5,30,2,137,0,152.9,5,11,15999,44,1080,9,1,2,13.0,2,2,4,11,4,3,16,6,75.6,128.0,6,3,5000,22,1 66 | 3,10,15,130.0,370,1.6,7.9,5,2.0,3,5.0,30,1,347,0,136.6,5,3,16999,17,1080,9,3,7,13.0,1,2,2,2,4,4,23,3,69.8,64.0,2,10,2600,20,1 67 | 0,10,29,145.0,456,1.7,7.3,2,8.0,3,5.0,30,2,22,0,142.0,5,3,17810,21,1080,1,1,2,16.0,2,3,4,11,4,4,23,6,70.0,128.0,5,3,3000,22,1 68 | 0,8,29,169.0,330,1.7,10.9,2,5.0,3,5.5,30,2,48,0,148.0,5,11,18499,18,1080,9,1,2,21.0,1,2,4,3,6,4,16,7,75.0,128.0,6,3,3630,22,1 69 | 1,8,29,135.0,488,1.5,7.3,5,5.0,3,5.0,30,3,0,0,145.5,21,9,18798,13,1080,6,1,2,13.0,1,2,2,8,6,4,9,0,72.6,128.0,5,3,2400,22,1 70 | 0,10,34,147.0,682,1.7,7.9,5,5.0,3,5.5,30,3,0,0,150.3,20,9,19000,11,1080,1,1,3,13.0,1,2,2,8,4,4,0,3,77.4,128.0,5,7,2600,16,1 71 | 0,8,38,150.0,420,1.5,8.0,5,4.0,3,5.5,30,3,18,0,157.7,26,9,19490,12,1080,9,1,2,13.0,2,2,2,5,4,4,16,3,78.9,2048.0,6,3,2800,19,1 72 | 0,10,34,165.0,170,1.5,7.6,5,4.0,3,5.5,30,1,13,0,157.7,26,9,19890,7,1080,4,3,2,13.0,6,2,2,5,4,4,7,3,79.7,128.0,5,3,2600,16,1 73 | 3,8,16,162.0,360,2.5,8.9,2,5.0,3,5.5,30,4,22,0,152.9,5,3,19999,38,2160,9,1,2,13.0,2,3,6,12,6,1,16,6,75.9,0.0,4,4,3100,15,1 74 | 3,10,17,112.0,250,1.3,7.6,2,1.2,3,4.0,30,1,1304,2,123.8,12,11,20397,10,1080,2,1,2,8.0,2,1,2,1,0,1,13,6,58.6,0.0,11,13,1560,13,1 75 | 0,8,29,183.0,687,1.7,8.0,5,4.0,3,5.5,30,2,131,0,158.0,26,11,21300,19,1080,9,1,9,13.0,2,2,2,5,4,4,16,6,77.5,128.0,5,3,2600,22,1 76 | 3,10,16,145.0,390,1.9,8.1,5,2.0,3,5.1,30,1,22,0,142.0,5,9,21999,21,2160,1,3,7,16.0,1,2,2,2,6,4,22,7,72.5,128.0,4,9,2800,15,0 77 | 0,8,34,187.0,635,1.7,8.2,5,13.0,3,6.0,30,3,26,0,164.2,20,9,21999,13,1080,1,1,2,13.0,1,2,2,8,4,4,14,3,79.6,200.0,5,3,2930,16,1 78 | 0,8,9,175.0,360,1.8,9.9,2,5.0,3,5.5,30,4,100,0,151.8,5,9,22999,38,2160,9,1,2,13.0,2,4,6,12,6,1,16,6,74.9,0.0,6,12,3300,5,1 79 | 1,8,24,157.0,620,2.2,8.5,1,8.0,3,5.2,30,3,29,0,143.2,26,9,22999,23,1080,9,4,2,20.0,2,3,2,15,4,4,20,6,71.9,128.0,5,3,3100,11,0 80 | 0,8,14,175.0,618,1.8,9.3,2,8.0,4,5.5,30,2,0,0,154.0,5,11,24499,31,2160,9,4,1,21.0,4,3,6,11,8,6,16,6,76.5,0.0,6,3,3600,8,0 81 | 3,7,41,155.0,180,1.6,7.1,5,5.0,3,5.2,30,3,75,0,144.8,7,9,24900,16,1080,1,0,7,13.0,2,2,2,2,4,4,20,3,71.0,128.0,6,3,2900,72,1 82 | 1,8,3,129.0,598,1.8,7.3,2,4.0,3,5.15,30,1,0,0,144.6,7,9,24999,27,2160,1,1,2,16.0,2,4,4,6,6,2,21,6,69.2,0.0,9,0,3000,2,0 83 | 4,8,14,179.0,410,1.8,11.1,0,5.0,3,5.7,30,1,4,0,153.9,5,9,24999,19,2160,10,4,12,21.0,1,3,6,3,8,4,26,6,76.2,128.0,6,3,3000,3,1 84 | 3,8,9,144.0,590,2.0,6.9,5,2.2,3,5.2,30,2,126,0,146.0,25,3,25500,17,2160,1,1,1,20.7,1,3,4,8,6,4,20,7,72.0,128.0,5,3,2930,5,1 85 | 1,10,28,142.6,504,2.0,7.6,5,13.0,3,5.0,30,3,69,0,145.0,21,9,25500,12,216,1,1,2,21.2,1,3,2,8,4,4,23,1,72.0,200.0,5,3,2600,14,0 86 | 0,10,25,145.0,250,2.0,6.6,5,16.0,3,5.5,30,4,19,0,151.8,7,11,27580,24,1080,9,1,0,13.0,2,4,6,10,4,3,16,6,74.3,128.0,6,3,2850,90,1 87 | 3,10,11,176.0,200,2.7,8.5,5,3.7,3,5.7,30,1,226,0,153.5,0,3,29900,20,216,10,3,7,16.0,1,3,4,2,6,4,24,6,78.6,128.0,4,3,3220,9,1 88 | 0,10,21,168.0,420,2.3,8.3,5,2.0,3,5.7,30,0,502,0,151.2,5,3,29990,21,2160,9,3,7,13.0,1,3,4,2,6,4,15,3,79.2,64.0,3,9,3200,52,1 89 | 4,6,14,155.0,410,1.8,6.3,2,8.0,3,5.5,30,3,0,0,148.9,10,9,30947,19,2160,10,5,1,16.0,1,3,4,9,8,4,28,6,76.1,2048.0,6,3,3000,8,1 90 | 3,8,9,178.0,440,2.0,7.3,2,8.0,3,5.7,30,1,87,0,159.3,11,9,31999,23,2160,8,1,0,12.3,2,3,4,15,4,2,25,6,77.8,0.0,9,3,3450,5,1 91 | 3,7,8,138.0,354,2.1,6.8,2,5.0,3,5.1,30,1,349,0,143.4,7,9,33900,17,2160,10,1,7,16.0,1,3,4,2,6,2,31,3,70.5,0.0,5,3,2550,4,1 92 | 3,8,9,169.0,500,2.0,9.2,2,5.0,3,5.4,30,4,22,0,149.8,23,11,34999,25,2160,10,1,0,21.0,1,3,4,3,6,1,29,6,78.0,0.0,7,3,3760,5,0 93 | 3,10,6,113.0,240,1.84,7.6,2,1.2,3,4.0,30,1,0,2,123.8,3,6,34999,14,2160,13,1,1,12.0,2,1,2,1,1,2,13,6,58.6,0.0,14,13,1624,1,0 94 | 4,8,14,136.0,360,1.8,7.9,1,5.0,3,5.2,30,1,24,0,147.0,5,9,35900,14,2160,9,4,2,12.3,4,2,2,9,8,2,19,6,72.6,0.0,9,11,2700,8,1 95 | 3,10,12,129.0,250,1.4,6.9,2,1.3,3,4.7,60,1,410,2,138.1,16,9,36499,14,1080,7,1,2,8.0,2,1,2,1,0,2,13,3,67.0,0.0,12,6,1810,7,1 96 | 3,10,12,129.0,250,1.4,6.9,2,1.2,3,4.7,60,4,39,2,138.1,16,11,36999,14,1080,9,1,2,8.0,2,1,2,1,0,1,13,6,67.0,0.0,12,13,1810,7,1 97 | 3,10,28,168.0,580,2.2,9.6,2,4.0,3,5.2,30,3,11,0,151.0,7,7,37766,20,2160,10,4,10,20.0,4,3,4,5,10,4,30,6,72.0,256.0,5,3,2840,56,0 98 | 3,12,16,149.0,410,2.5,8.9,3,2.1,3,5.5,30,3,0,0,146.3,5,1,38000,19,2160,10,3,1,13.0,5,3,4,9,7,4,28,6,74.6,32.0,4,3,3000,15,1 99 | 0,8,9,154.0,340,2.0,7.3,6,5.0,2,5.2,30,3,9,0,146.0,24,7,39890,13,2160,9,4,1,23.0,4,3,4,8,5,6,20,1,72.0,256.0,6,3,2900,5,1 100 | 3,2,8,132.0,362,1.5,7.0,5,5.0,4,5.1,30,1,0,0,142.1,9,7,40900,14,2160,10,1,7,16.0,1,3,4,2,7,4,32,6,70.1,64.0,5,3,2600,4,1 101 | 3,10,12,129.0,250,1.4,6.9,2,1.2,3,4.7,60,4,39,2,138.1,16,11,48329,14,1080,7,1,2,8.0,2,1,6,1,0,1,13,6,67.0,0.0,12,13,1810,7,1 102 | 0,5,3,152.0,242,2.3,7.9,5,5.0,3,5.1,30,4,150,0,142.4,7,9,48900,22,2160,10,1,8,12.0,1,4,4,2,6,6,32,1,69.6,200.0,9,3,3000,101,1 103 | 3,10,6,143.0,240,1.8,7.1,2,5.0,3,4.7,30,4,100,2,138.3,16,11,49499,14,2160,7,1,2,12.0,2,2,2,1,0,1,13,6,67.1,0.0,14,13,1715,1,1 104 | 0,7,43,171.0,330,2.1,7.6,5,5.0,3,5.7,30,1,107,0,153.2,7,9,50895,22,2160,8,1,7,16.0,2,4,4,2,4,2,25,6,76.1,0.0,6,3,3450,4,1 105 | 3,8,9,180.0,600,2.0,7.8,6,5.0,2,5.5,30,3,0,0,154.4,22,7,52699,19,2160,11,4,2,23.0,4,3,4,8,12,6,33,1,75.8,256.0,6,3,3430,5,1 106 | 3,10,14,192.0,540,1.8,9.4,2,2.0,2,5.4,30,1,0,0,147.0,26,7,54900,15,2160,10,4,0,18.0,4,3,4,25,3,4,29,6,77.2,256.0,6,3,3410,8,1 107 | 0,5,3,157.0,400,2.3,7.7,5,5.0,3,5.5,30,4,144,0,150.9,7,9,56900,27,2160,10,1,8,12.0,1,4,4,2,6,6,27,1,72.6,200.0,9,3,3600,101,1 108 | 3,10,6,192.0,384,1.8,7.3,2,5.0,3,5.5,30,4,81,2,158.2,16,11,59000,24,2160,7,1,2,12.0,2,2,2,1,0,1,16,6,77.9,0.0,14,13,2750,1,1 109 | 3,10,12,129.0,250,1.4,6.9,2,1.2,3,4.7,60,4,39,2,138.1,16,11,64500,14,1080,7,1,2,8.0,2,1,1,1,0,1,13,6,67.0,0.0,12,13,1810,7,1 110 | 2,8,3,158.0,400,2.2,7.4,6,8.0,3,5.5,30,1,0,0,152.7,7,8,27999,40,2160,9,1,6,16.0,4,6,6,12,6,5,16,6,74.7,0.0,10,12,3000,2,0 111 | -------------------------------------------------------------------------------- /PadhAI-Download-Materials/weights_viz-1553414815805.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/satyajitghana/PadhAI-Course/fd72c0e359b2d0599d52210b33a692a4210c4c1b/PadhAI-Download-Materials/weights_viz-1553414815805.gif -------------------------------------------------------------------------------- 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DL#101 - Getting Started 19 | 20 | completed on: 29/10/2019 21 | 22 | certificate: 23 | - [DL101-GuviCertification-u3793g5r7n16y5M2U2](certificates/DL101-GuviCertification-u3793g5r7n16y5M2U2.pdf) 24 | 25 | colab-notebooks: 26 | - [01_IntroToGoogleColab](colab-notebooks/01_IntroToGoogleColab.ipynb) 27 | - [02_MorePythonBasics](colab-notebooks/02_MorePythonBasics.ipynb) 28 | - [03_Vector](colab-notebooks/03_Vector.ipynb) 29 | 30 | 31 | ## 2. DL#102 - Primitive Neurons 32 | 33 | completed on: 29/10/2019 34 | 35 | certificate: 36 | - [DL102-GuviCertification-54n71RC9Y3s932y3z7](certificates/DL102-GuviCertification-54n71RC9Y3s932y3z7.pdf) 37 | 38 | colab-notebooks: 39 | - [04_MPNeuron](colab-notebooks/04_MPNeuron.ipynb) 40 | 41 | ## 3. DL#103 - Sigmoid Neuron 42 | 43 | completed on: 24/11/2019 44 | 45 | certificate: 46 | - [DL102-GuviCertification-54n71RC9Y3s932y3z7](certificates/DL102-GuviCertification-54n71RC9Y3s932y3z7.pdf) 47 | 48 | colab-notebooks: 49 | - [05_SigmoidNeuron](colab-notebooks/05_SigmoidNeuron.ipynb) 50 | - [06_SigmoidNeuronExample](colab-notebooks/06_SigmoidNeuronExample.ipynb) 51 | 52 | ## 4. DL#104 - Feedforward Neural Networks 53 | 54 | completed on: 25/11/2019 55 | 56 | certificate: 57 | - [DL104-GuviCertification-4775163N3z0T5DpE95](certificates/DL104-GuviCertification-4775163N3z0T5DpE95.pdf) 58 | 59 | colab-notebooks: 60 | - [07_FeedForwardNeuralNetwork](colab-notebooks/07_FeedForwardNeuralNetwork.ipynb) 61 | 62 | ## 5. DL#105 - Training Feedforward Neural Networks 63 | 64 | completed on: 10/12/2019 65 | 66 | certificate: 67 | - [DL105-GuviCertification-3219Q8mV9H75oPpJ57](certificates/DL105-GuviCertification-3219Q8mV9H75oPpJ57.pdf) 68 | 69 | colab-notebooks: 70 | - [08_Scalar_Backpropagation.ipynb](colab-notebooks/08_Scalar_Backpropagation.ipynb) 71 | - [09_VectorizedFeedforwardNeuralNetwork.ipynb](colab-notebooks/09_VectorizedFeedforwardNeuralNetwork.ipynb) 72 | 73 | ## 6. DL#106 - Optimization Algorithms 74 | 75 | completed on: 12/12/2019 76 | 77 | certificate: 78 | - [DL106-GuviCertification-551019LF6680gU47eh](certificates/DL106-GuviCertification-551019LF6680gU47eh.pdf) 79 | 80 | colab-notebooks: 81 | - [10_GDAlgorithms.ipynb](colab-notebooks/10_GDAlgorithms.ipynb) 82 | - [11_VectorizedGDAlgorithms.ipynb](colab-notebooks/11_VectorizedGDAlgorithms.ipynb) 83 | - [12_InitializationActivationFunction.ipynb](colab-notebooks/12_InitializationActivationFunction.ipynb) 84 | - [13_OverfittingAndRegularization.ipynb](colab-notebooks/13_OverfittingAndRegularization.ipynb) 85 | 86 | ## 7. DL#107 - Introduction to PyTorch 87 | 88 | completed on: 23/12/2019 89 | 90 | certificate: 91 | - [DL107-GuviCertification-4l5917H170YAC689z5](certificates/DL107-GuviCertification-4l5917H170YAC689z5.pdf) 92 | 93 | colab-notebooks: 94 | - [14_PyTorchIntro.ipynb](colab-notebooks/14_PyTorchIntro.ipynb) 95 | - [15_FFNetworksWithPyTorch.ipynb](colab-notebooks/15_FFNetworksWithPyTorch.ipynb) 96 | 97 | ## 8. DL#108 - Convolutional Neural Networks 98 | 99 | completed on: 26/12/2019 100 | 101 | certificate: 102 | - [DL108-GuviCertification-590umI28b179s79Pve](certificates/DL108-GuviCertification-590umI28b179s79Pve.pdf) 103 | 104 | colab-notebooks: 105 | - [16_PyTorchCNN.ipynb](colab-notebooks/16_PyTorchCNN.ipynb) 106 | 107 | ## 9. DL#109 - Deep Convolutional Neural Networks 108 | 109 | completed on: 30/12/2019 110 | 111 | certificate: 112 | - [DL109-GuviCertification-3T6hY1RK7v75t25u42](certificates/DL109-GuviCertification-3T6hY1RK7v75t25u42.pdf) 113 | 114 | colab-notebooks: 115 | - [17_LargeCNNs.ipynb](colab-notebooks/17_LargeCNNs.ipynb) 116 | - [18_CNNVisualization.ipynb](colab-notebooks/18_CNNVisualization.ipynb) 117 | - [19_BatchNorm_Dropout.ipynb](colab-notebooks/19_BatchNorm_Dropout.ipynb) 118 | - [20_HyperparameterTuning_MLFlow.ipynb](colab-notebooks/20_HyperparameterTuning_MLFlow.ipynb) 119 | 120 | ## 10. DL#110 - Sequence Models 121 | 122 | completed on: 01/01/2020 123 | 124 | certificate: 125 | - [DL110-GuviCertification-19g891p7375h721VlT.pdf](certificates/DL110-GuviCertification-19g891p7375h721VlT.pdf) 126 | 127 | colab-notebooks: 128 | - [21_RNNs.ipynb](colab-notebooks/21_RNNs.ipynb) 129 | - [22_BatchSeqModels.ipynb](colab-notebooks/22_BatchSeqModels.ipynb) 130 | 131 | ## 11. DL#111 - Encoder Decoder Models 132 | 133 | completed on: 01/01/2020 134 | 135 | certificate: 136 | - [DL111-GuviCertification-0A5R4s77281t16V628.pdf](certificates/DL111-GuviCertification-0A5R4s77281t16V628.pdf) 137 | 138 | colab-notebooks: 139 | - [23_EncoderDecoderArchitecture.ipynb](colab-notebooks/23_EncoderDecoderArchitecture.ipynb) 140 | 141 | ## 12. DL#112 - Introduction to Object Detection 142 | 143 | completed on: 01/01/2020 144 | 145 | certificate: 146 | - [DL112-GuviCertification-wLX9m77i0H6S7819M5.pdf](certificates/DL112-GuviCertification-wLX9m77i0H6S7819M5.pdf) 147 | 148 | -- 149 | 150 | ## Star History 151 | 152 | [![Star History Chart](https://api.star-history.com/svg?repos=satyajitghana/PadhAI-Course&type=Date)](https://star-history.com/#satyajitghana/PadhAI-Course&Date) 153 | -------------------------------------------------------------------------------- /certificates/DL101-GuviCertification-u3793g5r7n16y5M2U2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/satyajitghana/PadhAI-Course/fd72c0e359b2d0599d52210b33a692a4210c4c1b/certificates/DL101-GuviCertification-u3793g5r7n16y5M2U2.pdf -------------------------------------------------------------------------------- /certificates/DL102-GuviCertification-54n71RC9Y3s932y3z7.pdf: 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/colab-notebooks/data-sets/imagenet_labels.txt: -------------------------------------------------------------------------------- 1 | {0: 'tench, Tinca tinca', 2 | 1: 'goldfish, Carassius auratus', 3 | 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 4 | 3: 'tiger shark, Galeocerdo cuvieri', 5 | 4: 'hammerhead, hammerhead shark', 6 | 5: 'electric ray, crampfish, numbfish, torpedo', 7 | 6: 'stingray', 8 | 7: 'cock', 9 | 8: 'hen', 10 | 9: 'ostrich, Struthio camelus', 11 | 10: 'brambling, Fringilla montifringilla', 12 | 11: 'goldfinch, Carduelis carduelis', 13 | 12: 'house finch, linnet, Carpodacus mexicanus', 14 | 13: 'junco, snowbird', 15 | 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', 16 | 15: 'robin, American robin, Turdus migratorius', 17 | 16: 'bulbul', 18 | 17: 'jay', 19 | 18: 'magpie', 20 | 19: 'chickadee', 21 | 20: 'water ouzel, dipper', 22 | 21: 'kite', 23 | 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', 24 | 23: 'vulture', 25 | 24: 'great grey owl, great gray owl, Strix nebulosa', 26 | 25: 'European fire salamander, Salamandra salamandra', 27 | 26: 'common newt, Triturus vulgaris', 28 | 27: 'eft', 29 | 28: 'spotted salamander, Ambystoma maculatum', 30 | 29: 'axolotl, mud puppy, Ambystoma mexicanum', 31 | 30: 'bullfrog, Rana catesbeiana', 32 | 31: 'tree frog, tree-frog', 33 | 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', 34 | 33: 'loggerhead, loggerhead turtle, Caretta caretta', 35 | 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', 36 | 35: 'mud turtle', 37 | 36: 'terrapin', 38 | 37: 'box turtle, box tortoise', 39 | 38: 'banded gecko', 40 | 39: 'common iguana, iguana, Iguana iguana', 41 | 40: 'American chameleon, anole, Anolis carolinensis', 42 | 41: 'whiptail, whiptail lizard', 43 | 42: 'agama', 44 | 43: 'frilled lizard, Chlamydosaurus kingi', 45 | 44: 'alligator lizard', 46 | 45: 'Gila monster, Heloderma suspectum', 47 | 46: 'green lizard, Lacerta viridis', 48 | 47: 'African chameleon, Chamaeleo chamaeleon', 49 | 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', 50 | 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', 51 | 50: 'American alligator, Alligator mississipiensis', 52 | 51: 'triceratops', 53 | 52: 'thunder snake, worm snake, Carphophis amoenus', 54 | 53: 'ringneck snake, ring-necked snake, ring snake', 55 | 54: 'hognose snake, puff adder, sand viper', 56 | 55: 'green snake, grass snake', 57 | 56: 'king snake, kingsnake', 58 | 57: 'garter snake, grass snake', 59 | 58: 'water snake', 60 | 59: 'vine snake', 61 | 60: 'night snake, Hypsiglena torquata', 62 | 61: 'boa constrictor, Constrictor constrictor', 63 | 62: 'rock python, rock snake, Python sebae', 64 | 63: 'Indian cobra, Naja naja', 65 | 64: 'green mamba', 66 | 65: 'sea snake', 67 | 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', 68 | 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', 69 | 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', 70 | 69: 'trilobite', 71 | 70: 'harvestman, daddy longlegs, Phalangium opilio', 72 | 71: 'scorpion', 73 | 72: 'black and gold garden spider, Argiope aurantia', 74 | 73: 'barn spider, Araneus cavaticus', 75 | 74: 'garden spider, Aranea diademata', 76 | 75: 'black widow, Latrodectus mactans', 77 | 76: 'tarantula', 78 | 77: 'wolf spider, hunting spider', 79 | 78: 'tick', 80 | 79: 'centipede', 81 | 80: 'black grouse', 82 | 81: 'ptarmigan', 83 | 82: 'ruffed grouse, partridge, Bonasa umbellus', 84 | 83: 'prairie chicken, prairie grouse, prairie fowl', 85 | 84: 'peacock', 86 | 85: 'quail', 87 | 86: 'partridge', 88 | 87: 'African grey, African gray, Psittacus erithacus', 89 | 88: 'macaw', 90 | 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', 91 | 90: 'lorikeet', 92 | 91: 'coucal', 93 | 92: 'bee eater', 94 | 93: 'hornbill', 95 | 94: 'hummingbird', 96 | 95: 'jacamar', 97 | 96: 'toucan', 98 | 97: 'drake', 99 | 98: 'red-breasted merganser, Mergus serrator', 100 | 99: 'goose', 101 | 100: 'black swan, Cygnus atratus', 102 | 101: 'tusker', 103 | 102: 'echidna, spiny anteater, anteater', 104 | 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', 105 | 104: 'wallaby, brush kangaroo', 106 | 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', 107 | 106: 'wombat', 108 | 107: 'jellyfish', 109 | 108: 'sea anemone, anemone', 110 | 109: 'brain coral', 111 | 110: 'flatworm, platyhelminth', 112 | 111: 'nematode, nematode worm, roundworm', 113 | 112: 'conch', 114 | 113: 'snail', 115 | 114: 'slug', 116 | 115: 'sea slug, nudibranch', 117 | 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', 118 | 117: 'chambered nautilus, pearly nautilus, nautilus', 119 | 118: 'Dungeness crab, Cancer magister', 120 | 119: 'rock crab, Cancer irroratus', 121 | 120: 'fiddler crab', 122 | 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', 123 | 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', 124 | 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', 125 | 124: 'crayfish, crawfish, crawdad, crawdaddy', 126 | 125: 'hermit crab', 127 | 126: 'isopod', 128 | 127: 'white stork, Ciconia ciconia', 129 | 128: 'black stork, Ciconia nigra', 130 | 129: 'spoonbill', 131 | 130: 'flamingo', 132 | 131: 'little blue heron, Egretta caerulea', 133 | 132: 'American egret, great white heron, Egretta albus', 134 | 133: 'bittern', 135 | 134: 'crane', 136 | 135: 'limpkin, Aramus pictus', 137 | 136: 'European gallinule, Porphyrio porphyrio', 138 | 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', 139 | 138: 'bustard', 140 | 139: 'ruddy turnstone, Arenaria interpres', 141 | 140: 'red-backed sandpiper, dunlin, Erolia alpina', 142 | 141: 'redshank, Tringa totanus', 143 | 142: 'dowitcher', 144 | 143: 'oystercatcher, oyster catcher', 145 | 144: 'pelican', 146 | 145: 'king penguin, Aptenodytes patagonica', 147 | 146: 'albatross, mollymawk', 148 | 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', 149 | 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', 150 | 149: 'dugong, Dugong dugon', 151 | 150: 'sea lion', 152 | 151: 'Chihuahua', 153 | 152: 'Japanese spaniel', 154 | 153: 'Maltese dog, Maltese terrier, Maltese', 155 | 154: 'Pekinese, Pekingese, Peke', 156 | 155: 'Shih-Tzu', 157 | 156: 'Blenheim spaniel', 158 | 157: 'papillon', 159 | 158: 'toy terrier', 160 | 159: 'Rhodesian ridgeback', 161 | 160: 'Afghan hound, Afghan', 162 | 161: 'basset, basset hound', 163 | 162: 'beagle', 164 | 163: 'bloodhound, sleuthhound', 165 | 164: 'bluetick', 166 | 165: 'black-and-tan coonhound', 167 | 166: 'Walker hound, Walker foxhound', 168 | 167: 'English foxhound', 169 | 168: 'redbone', 170 | 169: 'borzoi, Russian wolfhound', 171 | 170: 'Irish wolfhound', 172 | 171: 'Italian greyhound', 173 | 172: 'whippet', 174 | 173: 'Ibizan hound, Ibizan Podenco', 175 | 174: 'Norwegian elkhound, elkhound', 176 | 175: 'otterhound, otter hound', 177 | 176: 'Saluki, gazelle hound', 178 | 177: 'Scottish deerhound, deerhound', 179 | 178: 'Weimaraner', 180 | 179: 'Staffordshire bullterrier, Staffordshire bull terrier', 181 | 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', 182 | 181: 'Bedlington terrier', 183 | 182: 'Border terrier', 184 | 183: 'Kerry blue terrier', 185 | 184: 'Irish terrier', 186 | 185: 'Norfolk terrier', 187 | 186: 'Norwich terrier', 188 | 187: 'Yorkshire terrier', 189 | 188: 'wire-haired fox terrier', 190 | 189: 'Lakeland terrier', 191 | 190: 'Sealyham terrier, Sealyham', 192 | 191: 'Airedale, Airedale terrier', 193 | 192: 'cairn, cairn terrier', 194 | 193: 'Australian terrier', 195 | 194: 'Dandie Dinmont, Dandie Dinmont terrier', 196 | 195: 'Boston bull, Boston terrier', 197 | 196: 'miniature schnauzer', 198 | 197: 'giant schnauzer', 199 | 198: 'standard schnauzer', 200 | 199: 'Scotch terrier, Scottish terrier, Scottie', 201 | 200: 'Tibetan terrier, chrysanthemum dog', 202 | 201: 'silky terrier, Sydney silky', 203 | 202: 'soft-coated wheaten terrier', 204 | 203: 'West Highland white terrier', 205 | 204: 'Lhasa, Lhasa apso', 206 | 205: 'flat-coated retriever', 207 | 206: 'curly-coated retriever', 208 | 207: 'golden retriever', 209 | 208: 'Labrador retriever', 210 | 209: 'Chesapeake Bay retriever', 211 | 210: 'German short-haired pointer', 212 | 211: 'vizsla, Hungarian pointer', 213 | 212: 'English setter', 214 | 213: 'Irish setter, red setter', 215 | 214: 'Gordon setter', 216 | 215: 'Brittany spaniel', 217 | 216: 'clumber, clumber spaniel', 218 | 217: 'English springer, English springer spaniel', 219 | 218: 'Welsh springer spaniel', 220 | 219: 'cocker spaniel, English cocker spaniel, cocker', 221 | 220: 'Sussex spaniel', 222 | 221: 'Irish water spaniel', 223 | 222: 'kuvasz', 224 | 223: 'schipperke', 225 | 224: 'groenendael', 226 | 225: 'malinois', 227 | 226: 'briard', 228 | 227: 'kelpie', 229 | 228: 'komondor', 230 | 229: 'Old English sheepdog, bobtail', 231 | 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', 232 | 231: 'collie', 233 | 232: 'Border collie', 234 | 233: 'Bouvier des Flandres, Bouviers des Flandres', 235 | 234: 'Rottweiler', 236 | 235: 'German shepherd, German shepherd dog, German police dog, alsatian', 237 | 236: 'Doberman, Doberman pinscher', 238 | 237: 'miniature pinscher', 239 | 238: 'Greater Swiss Mountain dog', 240 | 239: 'Bernese mountain dog', 241 | 240: 'Appenzeller', 242 | 241: 'EntleBucher', 243 | 242: 'boxer', 244 | 243: 'bull mastiff', 245 | 244: 'Tibetan mastiff', 246 | 245: 'French bulldog', 247 | 246: 'Great Dane', 248 | 247: 'Saint Bernard, St Bernard', 249 | 248: 'Eskimo dog, husky', 250 | 249: 'malamute, malemute, Alaskan malamute', 251 | 250: 'Siberian husky', 252 | 251: 'dalmatian, coach dog, carriage dog', 253 | 252: 'affenpinscher, monkey pinscher, monkey dog', 254 | 253: 'basenji', 255 | 254: 'pug, pug-dog', 256 | 255: 'Leonberg', 257 | 256: 'Newfoundland, Newfoundland dog', 258 | 257: 'Great Pyrenees', 259 | 258: 'Samoyed, Samoyede', 260 | 259: 'Pomeranian', 261 | 260: 'chow, chow chow', 262 | 261: 'keeshond', 263 | 262: 'Brabancon griffon', 264 | 263: 'Pembroke, Pembroke Welsh corgi', 265 | 264: 'Cardigan, Cardigan Welsh corgi', 266 | 265: 'toy poodle', 267 | 266: 'miniature poodle', 268 | 267: 'standard poodle', 269 | 268: 'Mexican hairless', 270 | 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', 271 | 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', 272 | 271: 'red wolf, maned wolf, Canis rufus, Canis niger', 273 | 272: 'coyote, prairie wolf, brush wolf, Canis latrans', 274 | 273: 'dingo, warrigal, warragal, Canis dingo', 275 | 274: 'dhole, Cuon alpinus', 276 | 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', 277 | 276: 'hyena, hyaena', 278 | 277: 'red fox, Vulpes vulpes', 279 | 278: 'kit fox, Vulpes macrotis', 280 | 279: 'Arctic fox, white fox, Alopex lagopus', 281 | 280: 'grey fox, gray fox, Urocyon cinereoargenteus', 282 | 281: 'tabby, tabby cat', 283 | 282: 'tiger cat', 284 | 283: 'Persian cat', 285 | 284: 'Siamese cat, Siamese', 286 | 285: 'Egyptian cat', 287 | 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', 288 | 287: 'lynx, catamount', 289 | 288: 'leopard, Panthera pardus', 290 | 289: 'snow leopard, ounce, Panthera uncia', 291 | 290: 'jaguar, panther, Panthera onca, Felis onca', 292 | 291: 'lion, king of beasts, Panthera leo', 293 | 292: 'tiger, Panthera tigris', 294 | 293: 'cheetah, chetah, Acinonyx jubatus', 295 | 294: 'brown bear, bruin, Ursus arctos', 296 | 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', 297 | 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', 298 | 297: 'sloth bear, Melursus ursinus, Ursus ursinus', 299 | 298: 'mongoose', 300 | 299: 'meerkat, mierkat', 301 | 300: 'tiger beetle', 302 | 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', 303 | 302: 'ground beetle, carabid beetle', 304 | 303: 'long-horned beetle, longicorn, longicorn beetle', 305 | 304: 'leaf beetle, chrysomelid', 306 | 305: 'dung beetle', 307 | 306: 'rhinoceros beetle', 308 | 307: 'weevil', 309 | 308: 'fly', 310 | 309: 'bee', 311 | 310: 'ant, emmet, pismire', 312 | 311: 'grasshopper, hopper', 313 | 312: 'cricket', 314 | 313: 'walking stick, walkingstick, stick insect', 315 | 314: 'cockroach, roach', 316 | 315: 'mantis, mantid', 317 | 316: 'cicada, cicala', 318 | 317: 'leafhopper', 319 | 318: 'lacewing, lacewing fly', 320 | 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", 321 | 320: 'damselfly', 322 | 321: 'admiral', 323 | 322: 'ringlet, ringlet butterfly', 324 | 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', 325 | 324: 'cabbage butterfly', 326 | 325: 'sulphur butterfly, sulfur butterfly', 327 | 326: 'lycaenid, lycaenid butterfly', 328 | 327: 'starfish, sea star', 329 | 328: 'sea urchin', 330 | 329: 'sea cucumber, holothurian', 331 | 330: 'wood rabbit, cottontail, cottontail rabbit', 332 | 331: 'hare', 333 | 332: 'Angora, Angora rabbit', 334 | 333: 'hamster', 335 | 334: 'porcupine, hedgehog', 336 | 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', 337 | 336: 'marmot', 338 | 337: 'beaver', 339 | 338: 'guinea pig, Cavia cobaya', 340 | 339: 'sorrel', 341 | 340: 'zebra', 342 | 341: 'hog, pig, grunter, squealer, Sus scrofa', 343 | 342: 'wild boar, boar, Sus scrofa', 344 | 343: 'warthog', 345 | 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', 346 | 345: 'ox', 347 | 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', 348 | 347: 'bison', 349 | 348: 'ram, tup', 350 | 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', 351 | 350: 'ibex, Capra ibex', 352 | 351: 'hartebeest', 353 | 352: 'impala, Aepyceros melampus', 354 | 353: 'gazelle', 355 | 354: 'Arabian camel, dromedary, Camelus dromedarius', 356 | 355: 'llama', 357 | 356: 'weasel', 358 | 357: 'mink', 359 | 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', 360 | 359: 'black-footed ferret, ferret, Mustela nigripes', 361 | 360: 'otter', 362 | 361: 'skunk, polecat, wood pussy', 363 | 362: 'badger', 364 | 363: 'armadillo', 365 | 364: 'three-toed sloth, ai, Bradypus tridactylus', 366 | 365: 'orangutan, orang, orangutang, Pongo pygmaeus', 367 | 366: 'gorilla, Gorilla gorilla', 368 | 367: 'chimpanzee, chimp, Pan troglodytes', 369 | 368: 'gibbon, Hylobates lar', 370 | 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', 371 | 370: 'guenon, guenon monkey', 372 | 371: 'patas, hussar monkey, Erythrocebus patas', 373 | 372: 'baboon', 374 | 373: 'macaque', 375 | 374: 'langur', 376 | 375: 'colobus, colobus monkey', 377 | 376: 'proboscis monkey, Nasalis larvatus', 378 | 377: 'marmoset', 379 | 378: 'capuchin, ringtail, Cebus capucinus', 380 | 379: 'howler monkey, howler', 381 | 380: 'titi, titi monkey', 382 | 381: 'spider monkey, Ateles geoffroyi', 383 | 382: 'squirrel monkey, Saimiri sciureus', 384 | 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', 385 | 384: 'indri, indris, Indri indri, Indri brevicaudatus', 386 | 385: 'Indian elephant, Elephas maximus', 387 | 386: 'African elephant, Loxodonta africana', 388 | 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', 389 | 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', 390 | 389: 'barracouta, snoek', 391 | 390: 'eel', 392 | 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', 393 | 392: 'rock beauty, Holocanthus tricolor', 394 | 393: 'anemone fish', 395 | 394: 'sturgeon', 396 | 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', 397 | 396: 'lionfish', 398 | 397: 'puffer, pufferfish, blowfish, globefish', 399 | 398: 'abacus', 400 | 399: 'abaya', 401 | 400: "academic gown, academic robe, judge's robe", 402 | 401: 'accordion, piano accordion, squeeze box', 403 | 402: 'acoustic guitar', 404 | 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', 405 | 404: 'airliner', 406 | 405: 'airship, dirigible', 407 | 406: 'altar', 408 | 407: 'ambulance', 409 | 408: 'amphibian, amphibious vehicle', 410 | 409: 'analog clock', 411 | 410: 'apiary, bee house', 412 | 411: 'apron', 413 | 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', 414 | 413: 'assault rifle, assault gun', 415 | 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', 416 | 415: 'bakery, bakeshop, bakehouse', 417 | 416: 'balance beam, beam', 418 | 417: 'balloon', 419 | 418: 'ballpoint, ballpoint pen, ballpen, Biro', 420 | 419: 'Band Aid', 421 | 420: 'banjo', 422 | 421: 'bannister, banister, balustrade, balusters, handrail', 423 | 422: 'barbell', 424 | 423: 'barber chair', 425 | 424: 'barbershop', 426 | 425: 'barn', 427 | 426: 'barometer', 428 | 427: 'barrel, cask', 429 | 428: 'barrow, garden cart, lawn cart, wheelbarrow', 430 | 429: 'baseball', 431 | 430: 'basketball', 432 | 431: 'bassinet', 433 | 432: 'bassoon', 434 | 433: 'bathing cap, swimming cap', 435 | 434: 'bath towel', 436 | 435: 'bathtub, bathing tub, bath, tub', 437 | 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', 438 | 437: 'beacon, lighthouse, beacon light, pharos', 439 | 438: 'beaker', 440 | 439: 'bearskin, busby, shako', 441 | 440: 'beer bottle', 442 | 441: 'beer glass', 443 | 442: 'bell cote, bell cot', 444 | 443: 'bib', 445 | 444: 'bicycle-built-for-two, tandem bicycle, tandem', 446 | 445: 'bikini, two-piece', 447 | 446: 'binder, ring-binder', 448 | 447: 'binoculars, field glasses, opera glasses', 449 | 448: 'birdhouse', 450 | 449: 'boathouse', 451 | 450: 'bobsled, bobsleigh, bob', 452 | 451: 'bolo tie, bolo, bola tie, bola', 453 | 452: 'bonnet, poke bonnet', 454 | 453: 'bookcase', 455 | 454: 'bookshop, bookstore, bookstall', 456 | 455: 'bottlecap', 457 | 456: 'bow', 458 | 457: 'bow tie, bow-tie, bowtie', 459 | 458: 'brass, memorial tablet, plaque', 460 | 459: 'brassiere, bra, bandeau', 461 | 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', 462 | 461: 'breastplate, aegis, egis', 463 | 462: 'broom', 464 | 463: 'bucket, pail', 465 | 464: 'buckle', 466 | 465: 'bulletproof vest', 467 | 466: 'bullet train, bullet', 468 | 467: 'butcher shop, meat market', 469 | 468: 'cab, hack, taxi, taxicab', 470 | 469: 'caldron, cauldron', 471 | 470: 'candle, taper, wax light', 472 | 471: 'cannon', 473 | 472: 'canoe', 474 | 473: 'can opener, tin opener', 475 | 474: 'cardigan', 476 | 475: 'car mirror', 477 | 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', 478 | 477: "carpenter's kit, tool kit", 479 | 478: 'carton', 480 | 479: 'car wheel', 481 | 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', 482 | 481: 'cassette', 483 | 482: 'cassette player', 484 | 483: 'castle', 485 | 484: 'catamaran', 486 | 485: 'CD player', 487 | 486: 'cello, violoncello', 488 | 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', 489 | 488: 'chain', 490 | 489: 'chainlink fence', 491 | 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', 492 | 491: 'chain saw, chainsaw', 493 | 492: 'chest', 494 | 493: 'chiffonier, commode', 495 | 494: 'chime, bell, gong', 496 | 495: 'china cabinet, china closet', 497 | 496: 'Christmas stocking', 498 | 497: 'church, church building', 499 | 498: 'cinema, movie theater, movie theatre, movie house, picture palace', 500 | 499: 'cleaver, meat cleaver, chopper', 501 | 500: 'cliff dwelling', 502 | 501: 'cloak', 503 | 502: 'clog, geta, patten, sabot', 504 | 503: 'cocktail shaker', 505 | 504: 'coffee mug', 506 | 505: 'coffeepot', 507 | 506: 'coil, spiral, volute, whorl, helix', 508 | 507: 'combination lock', 509 | 508: 'computer keyboard, keypad', 510 | 509: 'confectionery, confectionary, candy store', 511 | 510: 'container ship, containership, container vessel', 512 | 511: 'convertible', 513 | 512: 'corkscrew, bottle screw', 514 | 513: 'cornet, horn, trumpet, trump', 515 | 514: 'cowboy boot', 516 | 515: 'cowboy hat, ten-gallon hat', 517 | 516: 'cradle', 518 | 517: 'crane', 519 | 518: 'crash helmet', 520 | 519: 'crate', 521 | 520: 'crib, cot', 522 | 521: 'Crock Pot', 523 | 522: 'croquet ball', 524 | 523: 'crutch', 525 | 524: 'cuirass', 526 | 525: 'dam, dike, dyke', 527 | 526: 'desk', 528 | 527: 'desktop computer', 529 | 528: 'dial telephone, dial phone', 530 | 529: 'diaper, nappy, napkin', 531 | 530: 'digital clock', 532 | 531: 'digital watch', 533 | 532: 'dining table, board', 534 | 533: 'dishrag, dishcloth', 535 | 534: 'dishwasher, dish washer, dishwashing machine', 536 | 535: 'disk brake, disc brake', 537 | 536: 'dock, dockage, docking facility', 538 | 537: 'dogsled, dog sled, dog sleigh', 539 | 538: 'dome', 540 | 539: 'doormat, welcome mat', 541 | 540: 'drilling platform, offshore rig', 542 | 541: 'drum, membranophone, tympan', 543 | 542: 'drumstick', 544 | 543: 'dumbbell', 545 | 544: 'Dutch oven', 546 | 545: 'electric fan, blower', 547 | 546: 'electric guitar', 548 | 547: 'electric locomotive', 549 | 548: 'entertainment center', 550 | 549: 'envelope', 551 | 550: 'espresso maker', 552 | 551: 'face powder', 553 | 552: 'feather boa, boa', 554 | 553: 'file, file cabinet, filing cabinet', 555 | 554: 'fireboat', 556 | 555: 'fire engine, fire truck', 557 | 556: 'fire screen, fireguard', 558 | 557: 'flagpole, flagstaff', 559 | 558: 'flute, transverse flute', 560 | 559: 'folding chair', 561 | 560: 'football helmet', 562 | 561: 'forklift', 563 | 562: 'fountain', 564 | 563: 'fountain pen', 565 | 564: 'four-poster', 566 | 565: 'freight car', 567 | 566: 'French horn, horn', 568 | 567: 'frying pan, frypan, skillet', 569 | 568: 'fur coat', 570 | 569: 'garbage truck, dustcart', 571 | 570: 'gasmask, respirator, gas helmet', 572 | 571: 'gas pump, gasoline pump, petrol pump, island dispenser', 573 | 572: 'goblet', 574 | 573: 'go-kart', 575 | 574: 'golf ball', 576 | 575: 'golfcart, golf cart', 577 | 576: 'gondola', 578 | 577: 'gong, tam-tam', 579 | 578: 'gown', 580 | 579: 'grand piano, grand', 581 | 580: 'greenhouse, nursery, glasshouse', 582 | 581: 'grille, radiator grille', 583 | 582: 'grocery store, grocery, food market, market', 584 | 583: 'guillotine', 585 | 584: 'hair slide', 586 | 585: 'hair spray', 587 | 586: 'half track', 588 | 587: 'hammer', 589 | 588: 'hamper', 590 | 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', 591 | 590: 'hand-held computer, hand-held microcomputer', 592 | 591: 'handkerchief, hankie, hanky, hankey', 593 | 592: 'hard disc, hard disk, fixed disk', 594 | 593: 'harmonica, mouth organ, harp, mouth harp', 595 | 594: 'harp', 596 | 595: 'harvester, reaper', 597 | 596: 'hatchet', 598 | 597: 'holster', 599 | 598: 'home theater, home theatre', 600 | 599: 'honeycomb', 601 | 600: 'hook, claw', 602 | 601: 'hoopskirt, crinoline', 603 | 602: 'horizontal bar, high bar', 604 | 603: 'horse cart, horse-cart', 605 | 604: 'hourglass', 606 | 605: 'iPod', 607 | 606: 'iron, smoothing iron', 608 | 607: "jack-o'-lantern", 609 | 608: 'jean, blue jean, denim', 610 | 609: 'jeep, landrover', 611 | 610: 'jersey, T-shirt, tee shirt', 612 | 611: 'jigsaw puzzle', 613 | 612: 'jinrikisha, ricksha, rickshaw', 614 | 613: 'joystick', 615 | 614: 'kimono', 616 | 615: 'knee pad', 617 | 616: 'knot', 618 | 617: 'lab coat, laboratory coat', 619 | 618: 'ladle', 620 | 619: 'lampshade, lamp shade', 621 | 620: 'laptop, laptop computer', 622 | 621: 'lawn mower, mower', 623 | 622: 'lens cap, lens cover', 624 | 623: 'letter opener, paper knife, paperknife', 625 | 624: 'library', 626 | 625: 'lifeboat', 627 | 626: 'lighter, light, igniter, ignitor', 628 | 627: 'limousine, limo', 629 | 628: 'liner, ocean liner', 630 | 629: 'lipstick, lip rouge', 631 | 630: 'Loafer', 632 | 631: 'lotion', 633 | 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', 634 | 633: "loupe, jeweler's loupe", 635 | 634: 'lumbermill, sawmill', 636 | 635: 'magnetic compass', 637 | 636: 'mailbag, postbag', 638 | 637: 'mailbox, letter box', 639 | 638: 'maillot', 640 | 639: 'maillot, tank suit', 641 | 640: 'manhole cover', 642 | 641: 'maraca', 643 | 642: 'marimba, xylophone', 644 | 643: 'mask', 645 | 644: 'matchstick', 646 | 645: 'maypole', 647 | 646: 'maze, labyrinth', 648 | 647: 'measuring cup', 649 | 648: 'medicine chest, medicine cabinet', 650 | 649: 'megalith, megalithic structure', 651 | 650: 'microphone, mike', 652 | 651: 'microwave, microwave oven', 653 | 652: 'military uniform', 654 | 653: 'milk can', 655 | 654: 'minibus', 656 | 655: 'miniskirt, mini', 657 | 656: 'minivan', 658 | 657: 'missile', 659 | 658: 'mitten', 660 | 659: 'mixing bowl', 661 | 660: 'mobile home, manufactured home', 662 | 661: 'Model T', 663 | 662: 'modem', 664 | 663: 'monastery', 665 | 664: 'monitor', 666 | 665: 'moped', 667 | 666: 'mortar', 668 | 667: 'mortarboard', 669 | 668: 'mosque', 670 | 669: 'mosquito net', 671 | 670: 'motor scooter, scooter', 672 | 671: 'mountain bike, all-terrain bike, off-roader', 673 | 672: 'mountain tent', 674 | 673: 'mouse, computer mouse', 675 | 674: 'mousetrap', 676 | 675: 'moving van', 677 | 676: 'muzzle', 678 | 677: 'nail', 679 | 678: 'neck brace', 680 | 679: 'necklace', 681 | 680: 'nipple', 682 | 681: 'notebook, notebook computer', 683 | 682: 'obelisk', 684 | 683: 'oboe, hautboy, hautbois', 685 | 684: 'ocarina, sweet potato', 686 | 685: 'odometer, hodometer, mileometer, milometer', 687 | 686: 'oil filter', 688 | 687: 'organ, pipe organ', 689 | 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', 690 | 689: 'overskirt', 691 | 690: 'oxcart', 692 | 691: 'oxygen mask', 693 | 692: 'packet', 694 | 693: 'paddle, boat paddle', 695 | 694: 'paddlewheel, paddle wheel', 696 | 695: 'padlock', 697 | 696: 'paintbrush', 698 | 697: "pajama, pyjama, pj's, jammies", 699 | 698: 'palace', 700 | 699: 'panpipe, pandean pipe, syrinx', 701 | 700: 'paper towel', 702 | 701: 'parachute, chute', 703 | 702: 'parallel bars, bars', 704 | 703: 'park bench', 705 | 704: 'parking meter', 706 | 705: 'passenger car, coach, carriage', 707 | 706: 'patio, terrace', 708 | 707: 'pay-phone, pay-station', 709 | 708: 'pedestal, plinth, footstall', 710 | 709: 'pencil box, pencil case', 711 | 710: 'pencil sharpener', 712 | 711: 'perfume, essence', 713 | 712: 'Petri dish', 714 | 713: 'photocopier', 715 | 714: 'pick, plectrum, plectron', 716 | 715: 'pickelhaube', 717 | 716: 'picket fence, paling', 718 | 717: 'pickup, pickup truck', 719 | 718: 'pier', 720 | 719: 'piggy bank, penny bank', 721 | 720: 'pill bottle', 722 | 721: 'pillow', 723 | 722: 'ping-pong ball', 724 | 723: 'pinwheel', 725 | 724: 'pirate, pirate ship', 726 | 725: 'pitcher, ewer', 727 | 726: "plane, carpenter's plane, woodworking plane", 728 | 727: 'planetarium', 729 | 728: 'plastic bag', 730 | 729: 'plate rack', 731 | 730: 'plow, plough', 732 | 731: "plunger, plumber's helper", 733 | 732: 'Polaroid camera, Polaroid Land camera', 734 | 733: 'pole', 735 | 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', 736 | 735: 'poncho', 737 | 736: 'pool table, billiard table, snooker table', 738 | 737: 'pop bottle, soda bottle', 739 | 738: 'pot, flowerpot', 740 | 739: "potter's wheel", 741 | 740: 'power drill', 742 | 741: 'prayer rug, prayer mat', 743 | 742: 'printer', 744 | 743: 'prison, prison house', 745 | 744: 'projectile, missile', 746 | 745: 'projector', 747 | 746: 'puck, hockey puck', 748 | 747: 'punching bag, punch bag, punching ball, punchball', 749 | 748: 'purse', 750 | 749: 'quill, quill pen', 751 | 750: 'quilt, comforter, comfort, puff', 752 | 751: 'racer, race car, racing car', 753 | 752: 'racket, racquet', 754 | 753: 'radiator', 755 | 754: 'radio, wireless', 756 | 755: 'radio telescope, radio reflector', 757 | 756: 'rain barrel', 758 | 757: 'recreational vehicle, RV, R.V.', 759 | 758: 'reel', 760 | 759: 'reflex camera', 761 | 760: 'refrigerator, icebox', 762 | 761: 'remote control, remote', 763 | 762: 'restaurant, eating house, eating place, eatery', 764 | 763: 'revolver, six-gun, six-shooter', 765 | 764: 'rifle', 766 | 765: 'rocking chair, rocker', 767 | 766: 'rotisserie', 768 | 767: 'rubber eraser, rubber, pencil eraser', 769 | 768: 'rugby ball', 770 | 769: 'rule, ruler', 771 | 770: 'running shoe', 772 | 771: 'safe', 773 | 772: 'safety pin', 774 | 773: 'saltshaker, salt shaker', 775 | 774: 'sandal', 776 | 775: 'sarong', 777 | 776: 'sax, saxophone', 778 | 777: 'scabbard', 779 | 778: 'scale, weighing machine', 780 | 779: 'school bus', 781 | 780: 'schooner', 782 | 781: 'scoreboard', 783 | 782: 'screen, CRT screen', 784 | 783: 'screw', 785 | 784: 'screwdriver', 786 | 785: 'seat belt, seatbelt', 787 | 786: 'sewing machine', 788 | 787: 'shield, buckler', 789 | 788: 'shoe shop, shoe-shop, shoe store', 790 | 789: 'shoji', 791 | 790: 'shopping basket', 792 | 791: 'shopping cart', 793 | 792: 'shovel', 794 | 793: 'shower cap', 795 | 794: 'shower curtain', 796 | 795: 'ski', 797 | 796: 'ski mask', 798 | 797: 'sleeping bag', 799 | 798: 'slide rule, slipstick', 800 | 799: 'sliding door', 801 | 800: 'slot, one-armed bandit', 802 | 801: 'snorkel', 803 | 802: 'snowmobile', 804 | 803: 'snowplow, snowplough', 805 | 804: 'soap dispenser', 806 | 805: 'soccer ball', 807 | 806: 'sock', 808 | 807: 'solar dish, solar collector, solar furnace', 809 | 808: 'sombrero', 810 | 809: 'soup bowl', 811 | 810: 'space bar', 812 | 811: 'space heater', 813 | 812: 'space shuttle', 814 | 813: 'spatula', 815 | 814: 'speedboat', 816 | 815: "spider web, spider's web", 817 | 816: 'spindle', 818 | 817: 'sports car, sport car', 819 | 818: 'spotlight, spot', 820 | 819: 'stage', 821 | 820: 'steam locomotive', 822 | 821: 'steel arch bridge', 823 | 822: 'steel drum', 824 | 823: 'stethoscope', 825 | 824: 'stole', 826 | 825: 'stone wall', 827 | 826: 'stopwatch, stop watch', 828 | 827: 'stove', 829 | 828: 'strainer', 830 | 829: 'streetcar, tram, tramcar, trolley, trolley car', 831 | 830: 'stretcher', 832 | 831: 'studio couch, day bed', 833 | 832: 'stupa, tope', 834 | 833: 'submarine, pigboat, sub, U-boat', 835 | 834: 'suit, suit of clothes', 836 | 835: 'sundial', 837 | 836: 'sunglass', 838 | 837: 'sunglasses, dark glasses, shades', 839 | 838: 'sunscreen, sunblock, sun blocker', 840 | 839: 'suspension bridge', 841 | 840: 'swab, swob, mop', 842 | 841: 'sweatshirt', 843 | 842: 'swimming trunks, bathing trunks', 844 | 843: 'swing', 845 | 844: 'switch, electric switch, electrical switch', 846 | 845: 'syringe', 847 | 846: 'table lamp', 848 | 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', 849 | 848: 'tape player', 850 | 849: 'teapot', 851 | 850: 'teddy, teddy bear', 852 | 851: 'television, television system', 853 | 852: 'tennis ball', 854 | 853: 'thatch, thatched roof', 855 | 854: 'theater curtain, theatre curtain', 856 | 855: 'thimble', 857 | 856: 'thresher, thrasher, threshing machine', 858 | 857: 'throne', 859 | 858: 'tile roof', 860 | 859: 'toaster', 861 | 860: 'tobacco shop, tobacconist shop, tobacconist', 862 | 861: 'toilet seat', 863 | 862: 'torch', 864 | 863: 'totem pole', 865 | 864: 'tow truck, tow car, wrecker', 866 | 865: 'toyshop', 867 | 866: 'tractor', 868 | 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', 869 | 868: 'tray', 870 | 869: 'trench coat', 871 | 870: 'tricycle, trike, velocipede', 872 | 871: 'trimaran', 873 | 872: 'tripod', 874 | 873: 'triumphal arch', 875 | 874: 'trolleybus, trolley coach, trackless trolley', 876 | 875: 'trombone', 877 | 876: 'tub, vat', 878 | 877: 'turnstile', 879 | 878: 'typewriter keyboard', 880 | 879: 'umbrella', 881 | 880: 'unicycle, monocycle', 882 | 881: 'upright, upright piano', 883 | 882: 'vacuum, vacuum cleaner', 884 | 883: 'vase', 885 | 884: 'vault', 886 | 885: 'velvet', 887 | 886: 'vending machine', 888 | 887: 'vestment', 889 | 888: 'viaduct', 890 | 889: 'violin, fiddle', 891 | 890: 'volleyball', 892 | 891: 'waffle iron', 893 | 892: 'wall clock', 894 | 893: 'wallet, billfold, notecase, pocketbook', 895 | 894: 'wardrobe, closet, press', 896 | 895: 'warplane, military plane', 897 | 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', 898 | 897: 'washer, automatic washer, washing machine', 899 | 898: 'water bottle', 900 | 899: 'water jug', 901 | 900: 'water tower', 902 | 901: 'whiskey jug', 903 | 902: 'whistle', 904 | 903: 'wig', 905 | 904: 'window screen', 906 | 905: 'window shade', 907 | 906: 'Windsor tie', 908 | 907: 'wine bottle', 909 | 908: 'wing', 910 | 909: 'wok', 911 | 910: 'wooden spoon', 912 | 911: 'wool, woolen, woollen', 913 | 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', 914 | 913: 'wreck', 915 | 914: 'yawl', 916 | 915: 'yurt', 917 | 916: 'web site, website, internet site, site', 918 | 917: 'comic book', 919 | 918: 'crossword puzzle, crossword', 920 | 919: 'street sign', 921 | 920: 'traffic light, traffic signal, stoplight', 922 | 921: 'book jacket, dust cover, dust jacket, dust wrapper', 923 | 922: 'menu', 924 | 923: 'plate', 925 | 924: 'guacamole', 926 | 925: 'consomme', 927 | 926: 'hot pot, hotpot', 928 | 927: 'trifle', 929 | 928: 'ice cream, icecream', 930 | 929: 'ice lolly, lolly, lollipop, popsicle', 931 | 930: 'French loaf', 932 | 931: 'bagel, beigel', 933 | 932: 'pretzel', 934 | 933: 'cheeseburger', 935 | 934: 'hotdog, hot dog, red hot', 936 | 935: 'mashed potato', 937 | 936: 'head cabbage', 938 | 937: 'broccoli', 939 | 938: 'cauliflower', 940 | 939: 'zucchini, courgette', 941 | 940: 'spaghetti squash', 942 | 941: 'acorn squash', 943 | 942: 'butternut squash', 944 | 943: 'cucumber, cuke', 945 | 944: 'artichoke, globe artichoke', 946 | 945: 'bell pepper', 947 | 946: 'cardoon', 948 | 947: 'mushroom', 949 | 948: 'Granny Smith', 950 | 949: 'strawberry', 951 | 950: 'orange', 952 | 951: 'lemon', 953 | 952: 'fig', 954 | 953: 'pineapple, ananas', 955 | 954: 'banana', 956 | 955: 'jackfruit, jak, jack', 957 | 956: 'custard apple', 958 | 957: 'pomegranate', 959 | 958: 'hay', 960 | 959: 'carbonara', 961 | 960: 'chocolate sauce, chocolate syrup', 962 | 961: 'dough', 963 | 962: 'meat loaf, meatloaf', 964 | 963: 'pizza, pizza pie', 965 | 964: 'potpie', 966 | 965: 'burrito', 967 | 966: 'red wine', 968 | 967: 'espresso', 969 | 968: 'cup', 970 | 969: 'eggnog', 971 | 970: 'alp', 972 | 971: 'bubble', 973 | 972: 'cliff, drop, drop-off', 974 | 973: 'coral reef', 975 | 974: 'geyser', 976 | 975: 'lakeside, lakeshore', 977 | 976: 'promontory, headland, head, foreland', 978 | 977: 'sandbar, sand bar', 979 | 978: 'seashore, coast, seacoast, sea-coast', 980 | 979: 'valley, vale', 981 | 980: 'volcano', 982 | 981: 'ballplayer, baseball player', 983 | 982: 'groom, bridegroom', 984 | 983: 'scuba diver', 985 | 984: 'rapeseed', 986 | 985: 'daisy', 987 | 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", 988 | 987: 'corn', 989 | 988: 'acorn', 990 | 989: 'hip, rose hip, rosehip', 991 | 990: 'buckeye, horse chestnut, conker', 992 | 991: 'coral fungus', 993 | 992: 'agaric', 994 | 993: 'gyromitra', 995 | 994: 'stinkhorn, carrion fungus', 996 | 995: 'earthstar', 997 | 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', 998 | 997: 'bolete', 999 | 998: 'ear, spike, capitulum', 1000 | 999: 'toilet tissue, toilet paper, bathroom tissue'} -------------------------------------------------------------------------------- /colab-notebooks/data-sets/mobile_cleaned.csv: -------------------------------------------------------------------------------- 1 | sim_type,aperture,gpu_rank,weight,stand_by_time,processor_frequency,thickness,flash_type,front_camera_resolution,auto_focus,screen_size,frames_per_second,FM,no_of_reviews_in_gsmarena_in_week,os,phone_height,screen_protection,sim_size,price,talk_time,video_resolution,display_resolution,removable_battery,display_type,primary_camera_resolution,battery_type,ram_memory,internal_memory,brand_rank,no_of_cores,micro_sd_slot,screen_pixel_density,water_proof_rate,phone_width,expandable_memory,version,usb_type,battery_capacity,processor_rank,is_liked 2 | 0,12,55,155.0,250,1.3,10.5,5,2.0,0,4.5,15,3,0,0,132.9,26,3,3870,9,480,12,3,11,5.0,1,1,7,29,6,4,2,3,67.8,64.0,5,3,2000,125,1 3 | 0,1,55,132.0,300,1.3,10.6,5,0.3,1,4.0,30,2,6,0,124.5,26,5,4059,9,720,15,3,11,5.0,1,1,7,11,6,4,4,6,64.0,32.0,5,3,2000,165,1 4 | 0,9,55,142.0,329,1.5,8.5,5,2.0,3,5.0,30,2,20,0,145.5,4,3,4777,10,1080,4,3,1,5.04,2,1,7,27,6,4,9,6,72.0,32.0,6,3,2500,164,0 5 | 0,8,55,152.0,385,1.3,8.0,5,2.0,3,5.0,15,3,0,0,147.5,26,3,5799,19,720,17,3,2,5.0,1,1,7,4,6,4,1,3,75.1,32.0,6,3,3000,165,1 6 | 1,1,55,234.0,385,1.3,7.9,5,1.92,3,5.0,15,3,0,0,179.0,18,3,5990,11,720,17,3,1,5.0,1,1,7,4,6,4,1,6,91.0,32.0,6,3,3000,165,0 7 | 0,14,55,179.0,280,1.3,7.9,5,5.0,3,5.5,30,3,6,0,150.0,5,3,5999,22,720,0,0,2,8.0,1,1,7,4,6,4,7,3,71.0,32.0,6,3,2900,165,0 8 | 1,1,56,124.0,230,1.3,8.8,5,2.0,3,4.0,30,3,0,0,123.0,26,3,5999,11,1080,16,3,2,5.0,1,512,5,30,0,4,4,3,62.5,128.0,2,3,1700,163,1 9 | 0,8,41,154.0,182,1.0,8.1,5,2.0,3,5.0,30,5,0,0,132.0,26,3,6599,7,720,4,3,2,8.0,0,2,2,4,6,4,9,3,78.0,32.0,6,3,2000,92,1 10 | 1,8,41,214.0,182,1.0,8.2,5,2.0,3,5.0,30,3,39,0,172.0,18,3,6599,6,720,14,3,2,8.0,1,2,2,4,6,4,9,6,80.0,32.0,6,7,2000,92,1 11 | 0,1,55,155.0,435,1.3,11.6,5,2.0,3,5.0,15,2,5,0,142.1,5,3,6649,9,720,4,1,2,8.0,1,2,7,4,6,4,9,6,72.4,32.0,5,3,3000,125,1 12 | 0,1,55,169.0,514,1.3,7.9,2,2.0,3,5.0,30,3,5,0,152.0,26,3,6749,14,720,4,1,2,8.0,2,2,7,4,6,4,9,3,75.0,32.0,5,4,4000,126,1 13 | 0,10,55,137.0,300,1.3,7.9,5,5.0,3,5.0,30,2,42,1,141.6,18,3,6990,15,720,3,3,7,8.0,1,1,7,2,6,3,9,6,70.0,128.0,0,3,2600,166,0 14 | 0,1,50,135.0,280,1.4,8.0,7,5.0,3,5.3,30,3,10,0,148.0,26,3,6999,8,1080,4,3,2,13.0,1,1,7,20,4,4,8,2,74.0,32.0,4,3,2500,79,1 15 | 0,11,43,133.0,198,1.2,9.0,5,2.0,3,4.7,30,2,139,0,134.0,5,3,6999,7,1080,14,3,2,8.0,2,2,2,6,6,4,11,6,67.0,32.0,4,8,2200,94,1 16 | 0,8,41,142.0,200,1.3,8.9,5,5.0,3,5.0,30,3,94,0,140.8,21,3,6999,10,1080,4,1,2,13.0,1,3,2,18,6,6,9,6,70.4,32.0,6,3,2500,92,1 17 | 1,10,41,131.0,680,1.3,8.7,5,5.0,3,5.0,30,1,10,0,140.1,1,11,6999,23,1080,14,1,1,13.0,1,2,2,30,6,2,9,3,68.9,256.0,6,3,2500,92,1 18 | 1,10,47,152.0,576,1.2,8.6,5,5.0,3,5.0,30,3,28,3,144.0,26,3,7340,26,480,4,3,2,8.0,1,1,7,7,6,4,9,3,73.7,128.0,13,3,2200,132,1 19 | 0,10,43,128.0,264,1.2,8.2,5,2.0,3,5.0,30,2,126,0,141.0,26,3,7499,13,720,4,3,1,8.0,2,2,2,11,6,4,9,6,70.0,32.0,4,3,2300,94,1 20 | 0,10,41,130.0,180,1.3,8.4,5,2.0,3,4.5,30,3,0,0,69.0,18,3,7590,11,1080,4,3,7,5.0,1,1,7,2,6,6,5,6,136.5,128.0,6,7,2000,100,1 21 | 0,10,43,143.0,160,1.2,8.2,5,5.0,3,5.0,30,3,142,0,142.4,6,3,7790,7,1080,4,3,2,8.0,1,2,2,17,6,6,9,3,73.0,32.0,5,3,2230,94,1 22 | 0,1,55,152.0,450,1.2,9.4,2,5.0,3,5.0,30,2,0,0,143.0,5,3,7899,14,1080,4,1,2,8.0,1,2,7,4,6,4,9,6,71.8,32.0,6,3,4000,94,1 23 | 0,10,34,140.0,264,1.5,8.0,5,5.0,3,5.5,30,5,184,0,152.6,26,3,7914,39,1080,4,3,2,8.0,2,2,7,11,4,4,7,3,76.2,32.0,5,3,2900,16,1 24 | 2,1,38,125.0,354,1.3,7.6,5,5.0,3,5.0,30,5,10,0,144.6,14,11,7999,12,1080,9,1,2,13.0,2,3,4,27,4,2,23,6,72.0,128.0,6,5,2400,19,0 25 | 3,1,43,97.0,345,1.2,5.1,5,5.0,3,4.8,30,3,0,0,141.9,5,9,8490,8,1080,14,1,0,8.0,2,2,2,4,6,2,10,2,68.1,0.0,5,3,2000,94,1 26 | 0,10,29,150.0,322,1.5,8.2,5,5.0,3,5.0,30,3,113,0,142.0,2,12,8499,15,1080,9,3,2,13.0,2,2,2,11,4,6,23,6,71.0,32.0,6,4,2750,21,1 27 | 0,8,43,202.0,914,1.2,10.6,2,5.0,3,5.5,30,4,34,0,156.0,18,3,8999,38,1080,4,1,2,13.0,2,2,2,16,6,6,7,6,77.5,64.0,5,4,5000,94,1 28 | 0,8,43,170.0,456,1.2,10.8,2,5.0,3,5.5,30,2,189,0,152.5,7,3,8999,21,1080,9,3,2,13.0,2,2,2,16,6,4,7,6,77.2,128.0,5,3,3000,94,1 29 | 0,8,38,155.0,350,1.3,9.3,5,5.0,3,5.5,30,3,122,0,151.0,18,3,8999,38,1080,4,1,2,13.0,1,2,2,18,4,6,7,6,77.0,64.0,6,4,3000,19,1 30 | 0,1,44,160.0,617,1.7,9.6,5,5.0,3,5.0,30,3,0,0,150.1,26,12,9399,12,1080,4,3,9,8.0,2,1,7,5,4,4,9,3,72.7,32.0,5,3,2100,79,1 31 | 0,10,43,185.0,775,1.2,9.4,5,5.0,3,5.5,30,2,87,0,154.0,26,3,9499,38,1080,4,3,2,13.0,2,2,2,6,6,4,7,6,78.7,32.0,4,3,3100,94,1 32 | 0,8,55,141.0,218,1.2,7.6,5,8.0,3,5.0,30,2,9,0,142.7,26,10,9700,16,1080,18,1,11,8.0,3,1,2,10,6,4,3,6,71.7,128.0,1,3,2420,94,1 33 | 0,8,34,150.0,270,1.7,8.0,2,5.0,3,5.5,30,2,0,0,152.6,19,3,9715,30,1080,1,3,1,13.0,1,2,2,11,4,4,16,6,76.2,32.0,5,3,2900,16,1 34 | 0,8,43,155.0,250,1.4,11.6,2,5.0,3,5.0,30,2,760,0,142.1,5,3,9999,22,1080,4,1,2,13.0,1,2,2,3,6,4,9,7,72.4,32.0,6,3,2470,94,1 35 | 0,10,28,164.0,264,1.4,8.7,2,5.0,3,5.5,30,2,420,0,150.0,26,3,9999,20,1080,9,1,2,16.0,2,2,4,6,2,1,16,6,76.0,0.0,6,3,4050,12,1 36 | 0,10,28,164.0,265,1.4,8.7,2,5.0,3,5.5,30,2,420,0,150.0,18,3,9999,20,1080,9,1,2,16.0,2,2,4,6,2,3,16,6,76.0,0.0,6,3,4050,12,1 37 | 0,8,34,150.0,750,1.7,8.0,2,5.0,3,5.5,30,3,608,0,152.6,26,3,9999,36,1080,9,3,2,13.0,1,2,2,11,4,6,16,6,76.2,32.0,5,4,3000,90,1 38 | 0,10,34,138.0,521,1.7,8.2,5,5.0,3,5.0,30,3,20,0,146.9,26,9,9999,23,1080,4,3,2,13.0,2,1,7,5,4,4,9,6,70.9,32.0,4,3,2000,16,1 39 | 1,10,38,149.0,360,1.3,8.7,2,5.0,3,5.5,30,1,0,0,150.9,1,11,9999,20,1080,9,1,2,13.0,6,2,4,30,4,4,18,3,75.2,128.0,5,3,3100,19,1 40 | 2,0,27,128.0,490,1.7,8.8,5,5.0,3,5.0,30,3,0,0,140.8,18,11,9999,12,720,5,2,2,13.0,6,3,2,4,9,4,9,3,70.4,32.0,6,3,2100,19,0 41 | 0,4,41,172.0,350,1.2,8.2,5,5.0,3,5.5,30,5,0,0,151.8,18,3,10190,21,1080,4,3,12,13.0,1,2,7,2,6,6,7,6,77.5,128.0,6,3,3000,94,1 42 | 0,1,38,143.0,275,1.3,8.5,5,5.0,3,5.2,30,3,21,0,148.0,18,3,10899,10,1080,9,1,2,13.0,2,3,2,4,4,5,20,6,73.6,0.0,6,3,2900,90,1 43 | 0,8,28,169.0,496,2.2,7.5,5,5.0,3,5.5,30,1,26,0,151.1,17,2,10999,20,2160,9,1,2,13.0,2,3,4,19,4,1,9,6,74.2,0.0,8,1,3000,14,1 44 | 0,8,34,149.0,696,1.7,8.9,5,5.0,3,5.0,30,3,43,0,142.0,26,3,11090,46,1080,4,1,2,13.0,2,2,2,11,4,4,9,3,71.8,32.0,4,4,4000,16,1 45 | 0,8,41,153.2,261,1.3,8.3,2,8.0,3,5.5,30,3,0,0,151.9,5,3,11299,28,1080,0,3,2,13.0,2,3,2,14,6,4,7,3,76.7,32.0,6,3,3000,92,1 46 | 0,8,29,130.0,384,1.7,7.8,2,5.0,3,5.0,30,3,385,0,138.1,13,0,11999,35,1080,1,1,2,13.0,1,2,2,6,4,2,23,6,69.6,0.0,5,3,3120,22,1 47 | 0,10,38,160.0,265,1.3,9.2,2,5.0,3,5.5,30,2,1016,0,153.6,18,3,11999,22,1080,9,1,2,13.0,2,3,2,11,2,3,17,6,76.5,128.0,6,3,3300,12,1 48 | 0,10,29,155.0,342,1.5,11.6,2,5.0,3,5.0,30,3,0,0,142.1,5,0,12499,28,1080,4,1,12,13.0,1,2,2,3,4,4,34,5,72.4,32.0,6,2,2470,22,1 49 | 0,13,34,138.0,761,1.7,8.2,5,5.0,3,5.0,30,4,2,0,146.9,18,11,13349,30,1080,3,1,2,13.0,1,2,2,5,4,3,9,6,70.9,32.0,4,3,2000,16,1 50 | 0,3,41,148.0,566,1.0,9.3,5,5.0,3,5.0,30,2,0,0,141.0,5,3,13349,16,720,4,1,2,8.0,2,2,2,11,6,3,9,6,71.8,32.0,6,3,4000,92,1 51 | 0,8,29,155.0,620,1.5,9.8,2,5.0,3,5.5,30,3,671,0,153.0,5,3,13499,24,1080,9,4,1,16.0,2,3,3,3,9,4,16,3,76.6,128.0,9,3,3000,72,1 52 | 0,7,29,171.0,354,1.6,7.5,5,5.0,3,5.5,30,2,73,0,152.4,26,3,13990,23,1080,4,3,7,13.0,1,2,0,2,4,3,16,6,78.6,128.0,6,3,3000,72,1 53 | 0,8,28,150.0,337,2.0,8.7,5,5.0,3,5.5,30,2,25,0,156.8,5,4,13999,9,1080,9,1,2,13.0,1,4,2,4,4,3,16,6,78.1,64.0,6,3,3000,14,1 54 | 0,10,30,170.0,354,1.6,7.8,2,5.0,3,5.5,30,2,301,0,151.7,18,3,14249,23,1080,9,3,7,13.0,1,3,2,2,4,3,16,6,76.0,128.0,9,3,3300,72,1 55 | 0,9,43,141.0,345,1.2,7.3,6,5.0,4,5.5,30,5,0,0,151.3,7,11,14300,18,1080,4,4,7,13.0,1,2,2,2,11,4,7,3,77.2,64.0,4,3,2950,94,0 56 | 0,1,44,154.5,560,1.7,7.7,5,8.0,3,5.5,30,2,12,0,157.7,26,9,14614,12,1080,4,1,2,13.0,2,1,2,5,4,4,7,6,78.7,32.0,4,3,2600,79,1 57 | 1,1,44,171.8,1093,1.4,7.7,5,1.1,3,6.0,30,3,24,0,165.2,26,3,14880,30,1080,4,1,11,13.0,1,1,7,8,6,4,6,6,83.8,32.0,3,3,3000,129,1 58 | 1,10,29,229.0,480,1.5,7.6,4,5.0,3,6.8,30,3,0,0,186.6,18,3,14990,24,1080,1,1,1,13.0,2,2,4,11,4,4,12,6,96.6,64.0,5,3,3500,22,1 59 | 0,10,16,160.0,200,2.3,6.9,5,8.0,3,5.0,30,3,175,0,140.0,5,9,14999,24,1080,9,1,0,13.0,2,3,2,12,6,1,23,6,69.0,0.0,6,4,2525,15,1 60 | 3,6,16,149.0,280,2.5,8.9,5,8.0,3,5.0,30,2,33,0,139.2,26,3,14999,18,2160,1,1,2,13.0,1,3,2,6,6,2,23,6,68.5,0.0,4,3,3080,15,1 61 | 3,3,16,144.0,250,2.5,10.0,2,13.0,3,5.2,30,4,188,0,140.8,5,11,14999,24,2160,9,1,5,13.0,1,2,2,3,6,1,20,4,72.4,0.0,4,3,2300,15,1 62 | 2,10,16,175.0,840,2.5,8.9,3,8.0,3,5.5,30,1,6,0,155.7,15,11,15689,48,1080,9,1,4,13.0,2,3,6,11,6,0,16,3,77.3,0.0,6,14,4100,15,0 63 | 0,8,29,134.0,380,1.7,7.3,5,8.0,3,5.0,30,1,0,0,143.5,8,2,15950,20,1080,9,1,2,13.0,1,3,2,10,4,3,9,6,71.0,128.0,6,3,2500,21,1 64 | 0,7,30,170.0,354,1.6,7.8,5,5.0,3,5.5,30,3,301,0,151.7,26,3,15990,23,1080,0,3,7,13.0,1,3,2,2,4,4,7,6,76.0,32.0,9,3,3300,72,1 65 | 0,13,29,189.0,600,1.5,9.9,2,5.0,3,5.5,30,2,137,0,152.9,5,11,15999,44,1080,9,1,2,13.0,2,2,4,11,4,3,16,6,75.6,128.0,6,3,5000,22,1 66 | 3,10,15,130.0,370,1.6,7.9,5,2.0,3,5.0,30,1,347,0,136.6,5,3,16999,17,1080,9,3,7,13.0,1,2,2,2,4,4,23,3,69.8,64.0,2,10,2600,20,1 67 | 0,10,29,145.0,456,1.7,7.3,2,8.0,3,5.0,30,2,22,0,142.0,5,3,17810,21,1080,1,1,2,16.0,2,3,4,11,4,4,23,6,70.0,128.0,5,3,3000,22,1 68 | 0,8,29,169.0,330,1.7,10.9,2,5.0,3,5.5,30,2,48,0,148.0,5,11,18499,18,1080,9,1,2,21.0,1,2,4,3,6,4,16,7,75.0,128.0,6,3,3630,22,1 69 | 1,8,29,135.0,488,1.5,7.3,5,5.0,3,5.0,30,3,0,0,145.5,21,9,18798,13,1080,6,1,2,13.0,1,2,2,8,6,4,9,0,72.6,128.0,5,3,2400,22,1 70 | 0,10,34,147.0,682,1.7,7.9,5,5.0,3,5.5,30,3,0,0,150.3,20,9,19000,11,1080,1,1,3,13.0,1,2,2,8,4,4,0,3,77.4,128.0,5,7,2600,16,1 71 | 0,8,38,150.0,420,1.5,8.0,5,4.0,3,5.5,30,3,18,0,157.7,26,9,19490,12,1080,9,1,2,13.0,2,2,2,5,4,4,16,3,78.9,2048.0,6,3,2800,19,1 72 | 0,10,34,165.0,170,1.5,7.6,5,4.0,3,5.5,30,1,13,0,157.7,26,9,19890,7,1080,4,3,2,13.0,6,2,2,5,4,4,7,3,79.7,128.0,5,3,2600,16,1 73 | 3,8,16,162.0,360,2.5,8.9,2,5.0,3,5.5,30,4,22,0,152.9,5,3,19999,38,2160,9,1,2,13.0,2,3,6,12,6,1,16,6,75.9,0.0,4,4,3100,15,1 74 | 3,10,17,112.0,250,1.3,7.6,2,1.2,3,4.0,30,1,1304,2,123.8,12,11,20397,10,1080,2,1,2,8.0,2,1,2,1,0,1,13,6,58.6,0.0,11,13,1560,13,1 75 | 0,8,29,183.0,687,1.7,8.0,5,4.0,3,5.5,30,2,131,0,158.0,26,11,21300,19,1080,9,1,9,13.0,2,2,2,5,4,4,16,6,77.5,128.0,5,3,2600,22,1 76 | 3,10,16,145.0,390,1.9,8.1,5,2.0,3,5.1,30,1,22,0,142.0,5,9,21999,21,2160,1,3,7,16.0,1,2,2,2,6,4,22,7,72.5,128.0,4,9,2800,15,0 77 | 0,8,34,187.0,635,1.7,8.2,5,13.0,3,6.0,30,3,26,0,164.2,20,9,21999,13,1080,1,1,2,13.0,1,2,2,8,4,4,14,3,79.6,200.0,5,3,2930,16,1 78 | 0,8,9,175.0,360,1.8,9.9,2,5.0,3,5.5,30,4,100,0,151.8,5,9,22999,38,2160,9,1,2,13.0,2,4,6,12,6,1,16,6,74.9,0.0,6,12,3300,5,1 79 | 1,8,24,157.0,620,2.2,8.5,1,8.0,3,5.2,30,3,29,0,143.2,26,9,22999,23,1080,9,4,2,20.0,2,3,2,15,4,4,20,6,71.9,128.0,5,3,3100,11,0 80 | 0,8,14,175.0,618,1.8,9.3,2,8.0,4,5.5,30,2,0,0,154.0,5,11,24499,31,2160,9,4,1,21.0,4,3,6,11,8,6,16,6,76.5,0.0,6,3,3600,8,0 81 | 3,7,41,155.0,180,1.6,7.1,5,5.0,3,5.2,30,3,75,0,144.8,7,9,24900,16,1080,1,0,7,13.0,2,2,2,2,4,4,20,3,71.0,128.0,6,3,2900,72,1 82 | 1,8,3,129.0,598,1.8,7.3,2,4.0,3,5.15,30,1,0,0,144.6,7,9,24999,27,2160,1,1,2,16.0,2,4,4,6,6,2,21,6,69.2,0.0,9,0,3000,2,0 83 | 4,8,14,179.0,410,1.8,11.1,0,5.0,3,5.7,30,1,4,0,153.9,5,9,24999,19,2160,10,4,12,21.0,1,3,6,3,8,4,26,6,76.2,128.0,6,3,3000,3,1 84 | 3,8,9,144.0,590,2.0,6.9,5,2.2,3,5.2,30,2,126,0,146.0,25,3,25500,17,2160,1,1,1,20.7,1,3,4,8,6,4,20,7,72.0,128.0,5,3,2930,5,1 85 | 1,10,28,142.6,504,2.0,7.6,5,13.0,3,5.0,30,3,69,0,145.0,21,9,25500,12,216,1,1,2,21.2,1,3,2,8,4,4,23,1,72.0,200.0,5,3,2600,14,0 86 | 0,10,25,145.0,250,2.0,6.6,5,16.0,3,5.5,30,4,19,0,151.8,7,11,27580,24,1080,9,1,0,13.0,2,4,6,10,4,3,16,6,74.3,128.0,6,3,2850,90,1 87 | 3,10,11,176.0,200,2.7,8.5,5,3.7,3,5.7,30,1,226,0,153.5,0,3,29900,20,216,10,3,7,16.0,1,3,4,2,6,4,24,6,78.6,128.0,4,3,3220,9,1 88 | 0,10,21,168.0,420,2.3,8.3,5,2.0,3,5.7,30,0,502,0,151.2,5,3,29990,21,2160,9,3,7,13.0,1,3,4,2,6,4,15,3,79.2,64.0,3,9,3200,52,1 89 | 4,6,14,155.0,410,1.8,6.3,2,8.0,3,5.5,30,3,0,0,148.9,10,9,30947,19,2160,10,5,1,16.0,1,3,4,9,8,4,28,6,76.1,2048.0,6,3,3000,8,1 90 | 3,8,9,178.0,440,2.0,7.3,2,8.0,3,5.7,30,1,87,0,159.3,11,9,31999,23,2160,8,1,0,12.3,2,3,4,15,4,2,25,6,77.8,0.0,9,3,3450,5,1 91 | 3,7,8,138.0,354,2.1,6.8,2,5.0,3,5.1,30,1,349,0,143.4,7,9,33900,17,2160,10,1,7,16.0,1,3,4,2,6,2,31,3,70.5,0.0,5,3,2550,4,1 92 | 3,8,9,169.0,500,2.0,9.2,2,5.0,3,5.4,30,4,22,0,149.8,23,11,34999,25,2160,10,1,0,21.0,1,3,4,3,6,1,29,6,78.0,0.0,7,3,3760,5,0 93 | 3,10,6,113.0,240,1.84,7.6,2,1.2,3,4.0,30,1,0,2,123.8,3,6,34999,14,2160,13,1,1,12.0,2,1,2,1,1,2,13,6,58.6,0.0,14,13,1624,1,0 94 | 4,8,14,136.0,360,1.8,7.9,1,5.0,3,5.2,30,1,24,0,147.0,5,9,35900,14,2160,9,4,2,12.3,4,2,2,9,8,2,19,6,72.6,0.0,9,11,2700,8,1 95 | 3,10,12,129.0,250,1.4,6.9,2,1.3,3,4.7,60,1,410,2,138.1,16,9,36499,14,1080,7,1,2,8.0,2,1,2,1,0,2,13,3,67.0,0.0,12,6,1810,7,1 96 | 3,10,12,129.0,250,1.4,6.9,2,1.2,3,4.7,60,4,39,2,138.1,16,11,36999,14,1080,9,1,2,8.0,2,1,2,1,0,1,13,6,67.0,0.0,12,13,1810,7,1 97 | 3,10,28,168.0,580,2.2,9.6,2,4.0,3,5.2,30,3,11,0,151.0,7,7,37766,20,2160,10,4,10,20.0,4,3,4,5,10,4,30,6,72.0,256.0,5,3,2840,56,0 98 | 3,12,16,149.0,410,2.5,8.9,3,2.1,3,5.5,30,3,0,0,146.3,5,1,38000,19,2160,10,3,1,13.0,5,3,4,9,7,4,28,6,74.6,32.0,4,3,3000,15,1 99 | 0,8,9,154.0,340,2.0,7.3,6,5.0,2,5.2,30,3,9,0,146.0,24,7,39890,13,2160,9,4,1,23.0,4,3,4,8,5,6,20,1,72.0,256.0,6,3,2900,5,1 100 | 3,2,8,132.0,362,1.5,7.0,5,5.0,4,5.1,30,1,0,0,142.1,9,7,40900,14,2160,10,1,7,16.0,1,3,4,2,7,4,32,6,70.1,64.0,5,3,2600,4,1 101 | 3,10,12,129.0,250,1.4,6.9,2,1.2,3,4.7,60,4,39,2,138.1,16,11,48329,14,1080,7,1,2,8.0,2,1,6,1,0,1,13,6,67.0,0.0,12,13,1810,7,1 102 | 0,5,3,152.0,242,2.3,7.9,5,5.0,3,5.1,30,4,150,0,142.4,7,9,48900,22,2160,10,1,8,12.0,1,4,4,2,6,6,32,1,69.6,200.0,9,3,3000,101,1 103 | 3,10,6,143.0,240,1.8,7.1,2,5.0,3,4.7,30,4,100,2,138.3,16,11,49499,14,2160,7,1,2,12.0,2,2,2,1,0,1,13,6,67.1,0.0,14,13,1715,1,1 104 | 0,7,43,171.0,330,2.1,7.6,5,5.0,3,5.7,30,1,107,0,153.2,7,9,50895,22,2160,8,1,7,16.0,2,4,4,2,4,2,25,6,76.1,0.0,6,3,3450,4,1 105 | 3,8,9,180.0,600,2.0,7.8,6,5.0,2,5.5,30,3,0,0,154.4,22,7,52699,19,2160,11,4,2,23.0,4,3,4,8,12,6,33,1,75.8,256.0,6,3,3430,5,1 106 | 3,10,14,192.0,540,1.8,9.4,2,2.0,2,5.4,30,1,0,0,147.0,26,7,54900,15,2160,10,4,0,18.0,4,3,4,25,3,4,29,6,77.2,256.0,6,3,3410,8,1 107 | 0,5,3,157.0,400,2.3,7.7,5,5.0,3,5.5,30,4,144,0,150.9,7,9,56900,27,2160,10,1,8,12.0,1,4,4,2,6,6,27,1,72.6,200.0,9,3,3600,101,1 108 | 3,10,6,192.0,384,1.8,7.3,2,5.0,3,5.5,30,4,81,2,158.2,16,11,59000,24,2160,7,1,2,12.0,2,2,2,1,0,1,16,6,77.9,0.0,14,13,2750,1,1 109 | 3,10,12,129.0,250,1.4,6.9,2,1.2,3,4.7,60,4,39,2,138.1,16,11,64500,14,1080,7,1,2,8.0,2,1,1,1,0,1,13,6,67.0,0.0,12,13,1810,7,1 110 | 2,8,3,158.0,400,2.2,7.4,6,8.0,3,5.5,30,1,0,0,152.7,7,8,27999,40,2160,9,1,6,16.0,4,6,6,12,6,5,16,6,74.7,0.0,10,12,3000,2,0 111 | -------------------------------------------------------------------------------- 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Capstone Project/ProjectDetails.txt: -------------------------------------------------------------------------------- 1 | As explained in the next video to implement the final solution you need to build: 2 | 3 | 1) Text Detection: An object detection engine to detect signboard objects in the given image 4 | 5 | 2) Text Recognition: A CNN-RNN encoder decoder model to convert the cropped signboard into text (a sequence of characters) 6 | 7 | 3) Transliteration: A sequence-to-sequence with attention model for transliterating the above sequence of characters to the desired language. 8 | 9 | The datasets for each of these tasks is available at the below mentioned URLs: 10 | 11 | 1. Text Detection (Detecting bounding boxes containing text in the images) 12 | 13 | Train Set: https://drive.google.com/open?id=1E5kI8CLoC-XffqQMTWwSpBIPp1Wb2tne 14 | 15 | Test Set: https://drive.google.com/open?id=1Z6Qxr-q-F54iYB2G1AyoDymBh64f5REZ 16 | (428 real images with annotations) 17 | 18 | 2. Text Recognition (Getting the text from the detected crop) 19 | 20 | Train Set: https://drive.google.com/open?id=1E5kI8CLoC-XffqQMTWwSpBIPp1Wb2tne 21 | 22 | Test Set: https://drive.google.com/open?id=1C0-mc0WAIdssS5KJwOjghaWaqiImZZUr 23 | (1740 cropped word images from real pictures with annotations) 24 | 25 | 3. Transliteration (Transliterating Indic text to English) 26 | Link: https://github.com/GokulNC/NLP-Exercises/tree/master/Transliteration-Indian-Languages/Original-NEWS2012-data -------------------------------------------------------------------------------- /notes/13. Capstone Project/Signboard Translation from Vernacular Languages — AI4Bharat.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/satyajitghana/PadhAI-Course/fd72c0e359b2d0599d52210b33a692a4210c4c1b/notes/13. Capstone Project/Signboard Translation from Vernacular Languages — AI4Bharat.pdf -------------------------------------------------------------------------------- /notes/3. Sigmoid Neuron/Lesson+10_+Learning+-+Learning+by+guessing.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/satyajitghana/PadhAI-Course/fd72c0e359b2d0599d52210b33a692a4210c4c1b/notes/3. Sigmoid Neuron/Lesson+10_+Learning+-+Learning+by+guessing.pdf -------------------------------------------------------------------------------- /notes/3. Sigmoid Neuron/Lesson+11_+Data+&+Task.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/satyajitghana/PadhAI-Course/fd72c0e359b2d0599d52210b33a692a4210c4c1b/notes/3. Sigmoid Neuron/Lesson+11_+Data+&+Task.pdf -------------------------------------------------------------------------------- /notes/3. 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Sigmoid Neuron/Text - Non Text Classification.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"metadata":{"id":"a192xmwKijHs","colab_type":"code","colab":{},"trusted":true},"cell_type":"code","source":"import os\nimport sys\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image, ImageFilter\nfrom tqdm import tqdm_notebook\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import accuracy_score, mean_squared_error, log_loss, confusion_matrix\nimport matplotlib.pyplot as plt\n\nnp.random.seed(100)\nLEVEL = 'level_1'","execution_count":1,"outputs":[]},{"metadata":{"id":"fIuRdSezijHx","colab_type":"code","colab":{},"trusted":true},"cell_type":"code","source":"class SigmoidNeuron:\n \n def __init__(self):\n self.w = None\n self.b = None\n \n def perceptron(self, x):\n return np.dot(x, self.w.T) + self.b\n \n def sigmoid(self, x):\n return 1.0/(1.0 + np.exp(-x))\n \n def grad_w_mse(self, x, y):\n y_pred = self.sigmoid(self.perceptron(x))\n return (y_pred - y) * y_pred * (1 - y_pred) * x\n \n def grad_b_mse(self, x, y):\n y_pred = self.sigmoid(self.perceptron(x))\n return (y_pred - y) * y_pred * (1 - y_pred)\n \n def grad_w_ce(self, x, y):\n y_pred = self.sigmoid(self.perceptron(x))\n if y == 0:\n return y_pred * x\n elif y == 1:\n return -1 * (1 - y_pred) * x\n else:\n raise ValueError(\"y should be 0 or 1\")\n \n def grad_b_ce(self, x, y):\n y_pred = self.sigmoid(self.perceptron(x))\n if y == 0:\n return y_pred \n elif y == 1:\n return -1 * (1 - y_pred)\n else:\n raise ValueError(\"y should be 0 or 1\")\n \n def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn=\"mse\", display_loss=False):\n \n # initialise w, b\n if initialise:\n self.w = np.random.randn(1, X.shape[1])\n self.b = 0\n \n if display_loss:\n loss = {}\n \n for i in tqdm_notebook(range(epochs), total=epochs, unit=\"epoch\"):\n dw = 0\n db = 0\n for x, y in zip(X, Y):\n if loss_fn == \"mse\":\n dw += self.grad_w_mse(x, y)\n db += self.grad_b_mse(x, y) \n elif loss_fn == \"ce\":\n dw += self.grad_w_ce(x, y)\n db += self.grad_b_ce(x, y)\n self.w -= learning_rate * dw\n self.b -= learning_rate * db\n \n if display_loss:\n Y_pred = self.sigmoid(self.perceptron(X))\n if loss_fn == \"mse\":\n loss[i] = mean_squared_error(Y, Y_pred)\n elif loss_fn == \"ce\":\n loss[i] = log_loss(Y, Y_pred)\n \n if display_loss:\n plt.plot(loss.values())\n plt.xlabel('Epochs')\n if loss_fn == \"mse\":\n plt.ylabel('Mean Squared Error')\n elif loss_fn == \"ce\":\n plt.ylabel('Log Loss')\n plt.show()\n \n def predict(self, X):\n Y_pred = []\n for x in X:\n y_pred = self.sigmoid(self.perceptron(x))\n Y_pred.append(y_pred)\n return np.array(Y_pred)","execution_count":2,"outputs":[]},{"metadata":{"id":"VDe2wjl_ijH0","colab_type":"code","colab":{},"trusted":true},"cell_type":"code","source":"def read_all(folder_path, key_prefix=\"\"):\n '''\n It returns a dictionary with 'file names' as keys and 'flattened image arrays' as values.\n '''\n print(\"Reading:\")\n images = {}\n files = os.listdir(folder_path)\n for i, file_name in tqdm_notebook(enumerate(files), total=len(files)):\n file_path = os.path.join(folder_path, file_name)\n image_index = key_prefix + file_name[:-4]\n image = Image.open(file_path)\n image = image.convert(\"L\")\n images[image_index] = np.array(image.copy()).flatten()\n image.close()\n return images","execution_count":3,"outputs":[]},{"metadata":{"id":"mjuaN532ijH4","colab_type":"code","colab":{},"outputId":"4124ae5e-4a9c-44dc-8c84-7919e6927fe5","trusted":true},"cell_type":"code","source":"languages = ['ta', 'hi', 'en']\ntrainPath = \"../input/padhai-text-non-text-classification-level-1/\"+LEVEL+\"_train/\"+LEVEL+\"/\"\ntestPath = \"../input/padhai-text-non-text-classification-level-1/\"+LEVEL+\"_test/kaggle_\"+LEVEL\nimages_train = read_all(path+\"background\", key_prefix='bgr_') # change the path\nfor language in languages:\n images_train.update(read_all(path+language, key_prefix=language+\"_\" ))\nprint(len(images_train))\n\nimages_test = read_all(testPath, key_prefix='') # change the path\nprint(len(images_test))","execution_count":7,"outputs":[{"output_type":"stream","text":"Reading:\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=450), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c24aaca195d049d68bb5075bd5d4c2cf"}},"metadata":{}},{"output_type":"stream","text":"\nReading:\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=150), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1575515dd08e48cda106877c078828af"}},"metadata":{}},{"output_type":"stream","text":"\nReading:\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=150), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3fc4cd24fe1b4a1e856379e57d56eb52"}},"metadata":{}},{"output_type":"stream","text":"\nReading:\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=150), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa8fc3165fd34615a62971f499cf2d51"}},"metadata":{}},{"output_type":"stream","text":"\n900\nReading:\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=300), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4a756b6adc4e45ae82dc03adca35ddd9"}},"metadata":{}},{"output_type":"stream","text":"\n300\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"! cd ../input","execution_count":null,"outputs":[]},{"metadata":{"id":"xqcTJRmSijH-","colab_type":"code","colab":{},"outputId":"3a26f608-868b-498d-b18d-bfae2b452d4e","trusted":true},"cell_type":"code","source":"list(images_test.keys())[:5]","execution_count":null,"outputs":[]},{"metadata":{"id":"yQUKxV_FijIC","colab_type":"code","colab":{},"outputId":"e6f2538b-3285-49ea-d6fd-7fbadc2bb975","trusted":true},"cell_type":"code","source":"X_train = []\nY_train = []\nfor key, value in images_train.items():\n X_train.append(value)\n if key[:4] == \"bgr_\":\n Y_train.append(0)\n else:\n Y_train.append(1)\n\nID_test = []\nX_test = []\nfor key, value in images_test.items():\n ID_test.append(int(key))\n X_test.append(value)\n \n \nX_train = np.array(X_train)\nY_train = np.array(Y_train)\nX_test = np.array(X_test)\n\nprint(X_train.shape, Y_train.shape)\nprint(X_test.shape)","execution_count":null,"outputs":[]},{"metadata":{"id":"wy3IKx26ijIG","colab_type":"code","colab":{},"outputId":"f571f85d-1fe6-4a33-bcf1-ca1574aa3709","trusted":true},"cell_type":"code","source":"scaler = StandardScaler()\nX_scaled_train = scaler.fit_transform(X_train)\nX_scaled_test = scaler.transform(X_test)","execution_count":null,"outputs":[]},{"metadata":{"id":"eboQW2n1ijIK","colab_type":"code","colab":{},"outputId":"fa8fbf5d-5d5c-4463-aa3c-909d6698b9b0","trusted":true},"cell_type":"code","source":"sn_mse = SigmoidNeuron()\nsn_mse.fit(X_scaled_train, Y_train, epochs=100, learning_rate=0.015, loss_fn=\"mse\", display_loss=True)","execution_count":null,"outputs":[]},{"metadata":{"id":"547SFsgsijIO","colab_type":"code","colab":{},"outputId":"e6595d5e-a9e0-4b5f-f7b5-a56297bc69c0","trusted":true},"cell_type":"code","source":"sn_ce = SigmoidNeuron()\nsn_ce.fit(X_scaled_train, Y_train, epochs=100, learning_rate=0.015, loss_fn=\"ce\", display_loss=True)","execution_count":null,"outputs":[]},{"metadata":{"id":"_a3_-9zYijIS","colab_type":"code","colab":{},"trusted":true},"cell_type":"code","source":"def print_accuracy(sn):\n Y_pred_train = sn.predict(X_scaled_train)\n Y_pred_binarised_train = (Y_pred_train >= 0.5).astype(\"int\").ravel()\n accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)\n print(\"Train Accuracy : \", accuracy_train)\n print(\"-\"*50)","execution_count":null,"outputs":[]},{"metadata":{"id":"lqe2g9PLijIW","colab_type":"code","colab":{},"outputId":"0ce4b45c-78f5-4323-829b-db3e12c3f268","trusted":true},"cell_type":"code","source":"print_accuracy(sn_mse)\nprint_accuracy(sn_ce)","execution_count":null,"outputs":[]},{"metadata":{"id":"8IMv7SCUijIa","colab_type":"text"},"cell_type":"markdown","source":"## Sample Submission"},{"metadata":{"id":"4_pBsgYlijIb","colab_type":"code","colab":{},"trusted":true},"cell_type":"code","source":"Y_pred_test = sn_ce.predict(X_scaled_test)\nY_pred_binarised_test = (Y_pred_test >= 0.5).astype(\"int\").ravel()\n\nsubmission = {}\nsubmission['ImageId'] = ID_test\nsubmission['Class'] = Y_pred_binarised_test\n\nsubmission = pd.DataFrame(submission)\nsubmission = submission[['ImageId', 'Class']]\nsubmission = submission.sort_values(['ImageId'])\nsubmission.to_csv(\"submisision.csv\", index=False)","execution_count":null,"outputs":[]}],"metadata":{"colab":{"name":"Text - Non Text Classification.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":1} -------------------------------------------------------------------------------- /notes/4. 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