Day 5: Generative Adversarial Networks, Autoencoders, Recurrent Neural Networks, LSTM, GRU, sequence learning

Deep learning with R: Chapter 6.2 - Understanding RNN, LSTM, GRU layers

Recurrent Neural Networks | MIT 6.S191 - RNN and LSTM overview

Deep learning with R: Chapter 8 - Generarive deep learning

Deep Generative Modeling | MIT 6.S191 - GAN, Autoencoder, Variational autoencoder

Generative Models - This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning

A Friendly Introduction to Generative Adversarial Networks (GANs) - 20m video by Luis Serrano. GitHub repo

Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. “Generative Adversarial Networks.” ArXiv:1406.2661 [Cs, Stat], June 10, 2014

Goodfellow, Ian. “NIPS 2016 Tutorial: Generative Adversarial Networks.” ArXiv:1701.00160 [Cs], April 3, 2017

Pierre Baldi. “Autoencoders, Unsupervised Learning, and Deep Architectures.” In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, edited by Isabelle Guyon, Gideon Dror, Vincent Lemaire, Graham Taylor, and Daniel Silver, 37–49. PMLR, 2012 - Mathematical framework of autoencoders.