Deep Learning DIY
Module 0
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Software installation
Module 1
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Introduction & General Overview
Module 2a
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PyTorch tensors
Module 2b
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Automatic differentiation
Module 2c
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Automatic differentiation: VJP and intro to JAX
Module 3
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Loss functions for classification
Module 4
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Optimization for DL
Module 5
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Stacking layers
Module 6
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Convolutional neural network
Module 7
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Dataloading
Module 8a
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Embedding layers
Module 8b
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Collaborative filtering
Module 8c
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Word2vec
Module 9a
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Autoencoders
Module 9b
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UNets
Module 9c
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Flows
Module 10
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Generative adversarial networks
Module 11a
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Recurrent Neural Networks (theory)
Module 11b
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RNN in practice
Module 11c
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Batches with sequences in Pytorch
Module 12
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Attention and Transformers
Module 13
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Siamese Networks and Representation Learning
Module 14a
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The Benefits of Depth
Module 14b
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The Problems with Depth
Module 15
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Dropout
Module 16
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Batchnorm
Module 17
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Resnets
Module 18a
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Denoising Diffusion Probabilistic Models
Module 19
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Zero-shot classification with CLIP
Homeworks
Homework 1
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MLP from scratch
Homework 2
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Class Activation Map and adversarial examples
Homework 3
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VAE for MNIST clustering and generation
Bonus
Module
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Intro to Julia: Autodiff with dual numbers
Module
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Deep learning on graphs
Graph
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Node embeddings
Graph
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Signal processing on graphs
Graph
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Graph embeddings and GNNs
Post
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Spectral GCN
Post
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Convolutions from first principles
Post
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Invariant and equivariant networks
Graph
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Exploiting Graph Invariants in Deep Learning
Guest Lectures
Privacy Preserving ML
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Daniel Huynh
Module 8a - Embedding layers
Table of Contents
Embedding layers
Slides
Embedding layers
17:46
Dealing with symbolic data
18:31
One-hot encoding
22:46
Embeddings
27:40
Pytorch sparse layer
Slides
slides
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Last modified: July 11, 2024. Website built with
Franklin.jl
and the
Julia programming language
.
Last modified: July 11, 2024. Website built with Franklin.jl and the Julia programming language. 
