Deep Learning DIY
Module 0
-
Software installation
Module 1
-
Introduction & General Overview
Module 2a
-
PyTorch tensors
Module 2b
-
Automatic differentiation
Module 3
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Loss functions for classification
Module 4
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Optimization for DL
Module 5
-
Stacking layers
Module 6
-
Convolutional neural network
Module 7
-
Dataloading
Module 8a
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Embedding layers
Module 8b
-
Collaborative filtering
Module 8c
-
Word2vec
Module 9
-
Autoencoders
Module 10
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Generative adversarial networks
Module 11a
-
Recurrent Neural Networks (theory)
Module 11b
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RNN in practice
Module 11c
-
Batches with sequences in Pytorch
Module 12
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Intro to Julia: Autodiff with dual numbers
Module 13
-
Siamese Networks and Representation Learning
Module 14a
-
The Benefits of Depth
Module 14b
-
The Problems with Depth
Module 15
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Dropout
Module 16
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Batchnorm
Module 17
-
Resnets
Module
-
Node embeddings
Module
-
Signal processing on graphs
Module
-
Graph embeddings and GNNs
Homeworks
Homework 1
-
MLP from scratch
Homework 2
-
Class Activation Map and adversarial examples
Homework 3
-
VAE for MNIST clustering and generation
Extras
Bonus
-
Privacy Preserving ML
Bonus
-
Spectral GCN
Bonus
-
Convolutions from first principles
Bonus
-
Invariant and equivariant networks
Module 14a - The Benefits of Depth
Table of Contents
Benefits of Depth
Slides
Benefits of Depth
Slides
slides
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Last modified: January 11, 2022. Website built with
Franklin.jl
and the
Julia programming language
.
Last modified: January 11, 2022. Website built with Franklin.jl and the Julia programming language. 
