@_rohit_tiwari_: BOOKMARK Worthy: This guy literally built a full set of notebooks that actually teach deep learning, from basics to tra…

BOOKMARK Worthy: This guy literally built a full set of notebooks that actually teach deep learning, from basics to transformers. https://udlbook.github.io/udlbook/ Notebook 1.1 - Background mathematics Notebook 2.1 - Supervised learning Notebook 3.1 - Shallow networks I Notebook 3.2 - Shallow networks II Notebook 3.3 - Shallow network regions Notebook 3.4 - Activation functions Notebook 4.1 - Composing networks Notebook 4.2 - Clipping functions Notebook 4.3 - Deep networks Notebook 5.1 - Least squares loss Notebook 5.2 - Binary cross-entropy loss Notebook 5.3 - Multiclass cross-entropy loss Notebook 6.1 - Line search Notebook 6.2 - Gradient descent Notebook 6.3 - Stochastic gradient descent Notebook 6.4 - Momentum Notebook 6.5 - Adam Notebook 7.1 - Backpropagation in toy model Notebook 7.2 - Backpropagation Notebook 7.3 - Initialization Notebook 8.1 - MNIST-1D performance Notebook 8.2 - Bias-variance trade-off Notebook 8.3 - Double descent Notebook 8.4 - High-dimensional spaces Notebook 9.1 - L2 regularization Notebook 9.2 - Implicit regularization Notebook 9.3 - Ensembling Notebook 9.4 - Bayesian approach Notebook 9.5 - Augmentation Notebook 10.1 - 1D convolution Notebook 10.2 - Convolution for MNIST-1D Notebook 10.3 - 2D convolution Notebook 10.4 - Downsampling & upsampling Notebook 10.5 - Convolution for MNIST Notebook 11.1 - Shattered gradients Notebook 11.2 - Residual networks Notebook 11.3 - Batch normalization Notebook 12.1 - Self-attention Notebook 12.2 - Multi-head self-attention Notebook 12.3 - Tokenization Notebook 12.4 - Decoding strategies Notebook 13.1 - Encoding graphs Notebook 13.2 - Graph classification Notebook 13.3 - Neighborhood sampling Notebook 13.4 - Graph attention Notebook 15.1 - GAN toy example Notebook 15.2 - Wasserstein distance Notebook 16.1 - 1D normalizing flows Notebook 16.2 - Autoregressive flows Notebook 16.3 - Contraction mappings Notebook 17.1 - Latent variable models Notebook 17.2 - Reparameterization trick Notebook 17.3 - Importance sampling Notebook 18.1 - Diffusion encoder Notebook 18.2 - 1D diffusion model Notebook 18.3 - Reparameterized model Notebook 18.4 - Families of diffusion models Notebook 19.1 - Markov decision processes Notebook 19.2 - Dynamic programming Notebook 19.3 - Monte-Carlo methods Notebook 19.4 - Temporal difference methods Notebook 19.5 - Control variates Notebook 20.1 - Random data Notebook 20.2 - Full-batch gradient descent Notebook 20.3 - Lottery tickets Notebook 20.4 - Adversarial attacks Notebook 21.1 - Bias mitigation Notebook 21.2 - Explainability