This contains all the links mentioned in the different chapters of the book

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Chapter 1 Links

[1] BackPropagation: https://www.nature.com/articles/323533a0

[2] 3Blue1Brown: https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

[3] AI vs Kasparov: https://www.youtube.com/watch?v=NJarxpYyoFI&ab_channel=Eustake

[4] AI Playing Jeopardy: https://www.youtube.com/results?search_query=AI+plays+Jeopardy

[5] Libratus Poker: https://www.youtube.com/watch?v=jLXPGwJNLHk&ab_channel=Engadget

[6] Alpha GO paper: https://www.nature.com/articles/nature16961

[7] Open AI Dota paper: https://arxiv.org/abs/1912.06680

[8] Human vs AI in GO part 2: https://goattack.far.ai/pdfs/go_attack_paper.pdf

Chapter 2 Links

[9] Anaconda Downloads: https://www.anaconda.com/download/

[10] VS Code Download: https://code.visualstudio.com/download

[11] Installing Git: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git

[12] OOPS in Python: https://www.youtube.com/watch?v=JeznW_7DlB0&ab_channel=TechWithTim

Chapter 3 Links

[13] Write Regex expression from english text: https://www.autoregex.xyz/

Chapter 4 Links

[14] NN as Universal Approximators: https://ieeexplore.ieee.org/document/256500

[15] Paper on Neural Network pruning: https://arxiv.org/abs/2103.06460

[16] GATO: A generalised agent paper: https://arxiv.org/pdf/2205.06175.pdf

Chapter 5 Links

[17] StatsQuest video on PCA: https://www.youtube.com/watch?v=FgakZw6K1QQ&ab_channel=StatQuestwithJoshStarmer

[18] Understanding SMO, similar to SGD optimization: https://en.wikipedia.org/wiki/Sequential_minimal_optimization

[19] Optimization of Logistic regression: https://medium.com/aiguys/beautiful-maths-behind-logistic-regression-optimization-6cefd3ec1c91

####ERRORS:

Page 134: Let’s talk about the preceding optimization problem; it’s an optimization problem where we are trying to minimize (weights and biases) such that alphas are maximized. It’s a MIN(MAX) problem where we are trying to minimize the product of W(transpose) and W such that Y_k [WTX_k + b] >= 1.

Chapter 6 Links

[20] TensorFlow NN playgorund: https://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.19712&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false

Chapter 7 Links

[21] 3Blue1Brown Backpropagation video: https://www.youtube.com/watch?v=Ilg3gGewQ5U&ab_channel=3Blue1Brown

Chapter 8 Links

[22] Different pooling methods paper: https://arxiv.org/ftp/arxiv/papers/2009/2009.07485.pdf

[23] Stanford CNN video lecture: https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&index=5&ab_channel=StanfordUniversitySchoolofEngineering

[24] Stanford CNN architectures: https://www.youtube.com/watch?v=DAOcjicFr1Y&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&index=9&ab_channel=StanfordUniversitySchoolofEngineering

Chapter 9 Links

[25] TensorFlow Guide: https://www.tensorflow.org/guide/

Chapter 10 Links

[26] Weights and Biases tutorials: https://docs.wandb.ai/tutorials

[27] IIIT Pet dataset: https://www.robots.ox.ac.uk/~vgg/data/pets/

Chapter 11 Links

[28] Stanford RNN lecture: https://www.youtube.com/watch?v=6niqTuYFZLQ&ab_channel=StanfordUniversitySchoolofEngineering

[29] Statsquest video on LSTM: https://www.youtube.com/watch?v=YCzL96nL7j0&ab_channel=StatQuestwithJoshStarmer

[30] MIT: Transformer, and Attention: https://www.youtube.com/watch?v=ySEx_Bqxvvo&ab_channel=AlexanderAmini

[31] Stanford, self-attention and transformers: https://www.youtube.com/watch?v=ptuGllU5SQQ&ab_channel=StanfordOnline

[32] VAE explained: https://www.youtube.com/watch?v=9zKuYvjFFS8&t=530s&ab_channel=ArxivInsights

[33] Power of GANs: https://this-person-does-not-exist.com/en

[34] Stanford: Generative models: https://www.youtube.com/watch?v=5WoItGTWV54&ab_channel=StanfordUniversitySchoolofEngineering

[35] Ian Goodfellow on GANs: https://www.youtube.com/watch?v=Z6rxFNMGdn0&t=656s&ab_channel=LexFridman

For the latest developments on AI: https://medium.com/aiguys