Deep Learning DIY

Module 0 - Software installation Module 1 - Introduction & General Overview Module 2a - PyTorch tensors Module 2b - Automatic differentiation Module 2c - Automatic differentiation: VJP and intro to JAX Module 3 - Loss functions for classification Module 4 - Optimization for DL Module 5 - Stacking layers Module 6 - Convolutional neural network Module 7 - Dataloading Module 8a - Embedding layers Module 8b - Collaborative filtering Module 8c - Word2vec Module 9a - Autoencoders Module 9b - UNets Module 9c - Flows Module 10 - Generative adversarial networks Module 11a - Recurrent Neural Networks (theory) Module 11b - RNN in practice Module 11c - Batches with sequences in Pytorch Module 12 - Attention and Transformers Module 13 - Siamese Networks and Representation Learning Module 14a - The Benefits of Depth Module 14b - The Problems with Depth Module 15 - Dropout Module 16 - Batchnorm Module 17 - Resnets Module 18a - Denoising Diffusion Probabilistic Models Module 19 - Zero-shot classification with CLIP

Homeworks

Homework 1 - MLP from scratch Homework 2 - Class Activation Map and adversarial examples Homework 3 - VAE for MNIST clustering and generation

Bonus

Module - Intro to Julia: Autodiff with dual numbers Module - Deep learning on graphs Graph - Node embeddings Graph - Signal processing on graphs Graph - Graph embeddings and GNNs Post - Spectral GCN Post - Convolutions from first principles Post - Invariant and equivariant networks Graph - Exploiting Graph Invariants in Deep Learning

Guest Lectures

Privacy Preserving ML - Daniel Huynh

Module 17 - Resnets

Table of Contents

Resnets
Slides

Resnets

Slides

slides

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