Deep Learning DIY
Module 0
-
Software installation
Unit 1
Module 1
-
Introduction & General Overview
Module 2a
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PyTorch tensors
Module 2b
-
Automatic differentiation
Unit 2
Module 3
-
Loss functions for classification
Module 4
-
Optimization for DL
Module 5
-
Stacking layers
Homework 1
-
MLP from scratch
Unit 3
Module 6
-
Convolutional neural network
Module 7
-
Dataloading
Module 8a
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Embedding layers
Module 8b
-
Collaborative filtering
Homework 2
-
Class Activation Map and adversarial examples
Unit 4
Module 9
-
Autoencoders
Module 10
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Generative adversarial networks
Homework 3
-
VAE for MNIST clustering and generation
Unit 5
Module 11a
-
Recurrent Neural Networks (theory)
Module 11b
-
RNN in practice
Module 11c
-
Batches with sequences in Pytorch
Unit 6
Module 12
-
Intro to Julia: Autodiff with dual numbers
Module 13
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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 18
-
TBC
Module
-
Deep Learning on graphs (1)
Module
-
Deep Learning on graphs (2)
Module
-
Deep Learning on graphs (3)
Module
-
Privacy Preserving ML
Module 14a - The Benefits of Depth
Table of Contents
Benefits of Depth
Slides
Benefits of Depth
Slides
slides
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Last modified: March 03, 2021. Website built with
Franklin.jl
and the
Julia programming language
.
Last modified: March 03, 2021. Website built with Franklin.jl and the Julia programming language. 
