https://dataflowr.github.io/website/

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Deep Learning DIY

Module 0 - Software installation
Unit 1
Module 1 - Introduction & General Overview Module 2a - 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 - Embedding layers Module 8b - Collaborative filtering Homework 2
- Class Activation Map and adversarial examples
Unit 4
Module 9 - Autoencoders Module 10 - 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 - 
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

Deep Learning Do It Yourself!

This site collects resources to learn Deep Learning in the form of
Modules available through the sidebar on the left. As a student, you
can walk through the modules at your own pace and interact with
others thanks to the associated digital platforms. Then we hope
you'll become a contributor by adding modules to this site!

Curators

Marc Lelarge, Jill-Jenn Vie, Andrei Bursuc

For students

Pre-requisites:

  * Mathematics: basics of linear algebra, probability, differential
    calculus and optimization

  * Programming: basic proficiency Python

Annotation tool

  * hypothes.is allows you to annotate this website and the web in
    general. You'll find some hints for the practicals here!

Social interactions

  * Discord: discussion (we encourage you to hang out here during
    class!)

  * Forum: discussion

For contributors

Join the GitHub repo dataflowr/website and make a pull request. What
are pull requests?

Evaluation

Materials from this site is used for courses at ENS and X. To
validate these courses, please connect to the appropriate moodle:

  * ENS Moodle: (ENS and affiliated students)

  * X Moodle: (X and affiliated students)

[ENS_logo] [X_logo]
Edit this page on [logo-githu] Last modified: March 03, 2021. Website
built with Franklin.jl and the Julia programming language.