Deep Learning DIY
Module 0
-
Software installation
Unit 1
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
Unit 2
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
Homework 1
-
MLP from scratch
Unit 3
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
Homework 2
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Class Activation Map and adversarial examples
Unit 4
Module 9
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Autoencoders
Module 10
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Generative adversarial networks
Homework 3
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VAE for MNIST clustering and generation
Unit 5
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
Unit 6
Module 12
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Intro to Julia: Autodiff with dual numbers
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 18
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TBC
Module
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Deep Learning on graphs (1)
Module
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Deep Learning on graphs (2)
Module
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Deep Learning on graphs (3)
Module
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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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