Deep-Learning-Learning-Path

Foundation

TextBooks

• The Elements of Statistical Learning, Hastie, et al. [pdf]
• Deep Learning Book, MIT
• Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig.
• Information Theory, Inference, and Learning Algorithms, David J. C. MacKay, 2003.
• Pattern Recognition and Machine Learning, Christopher Bishop, 2006.
• Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, 2012.

Courses

• Machine Learning, Andrew Ng [[Coursera]] (https://www.coursera.org/learn/machine-learning#)
• Neural Networks for Machine Learning, University of Toronto, Geoffrey Hinton[[Coursera]] (https://www.coursera.org/learn/neural-networks/home/info)
• INFO8010 - Deep Learning, Gilles Louppe.

Tools

• Scikit-learn: http://scikit-learn.org/stable/index.html
• Tensorflow: https://www.tensorflow.org/

Schools

• DLSS [2018 Lecture Videos]

More Advanced Topics

Bayesian Inference

• Variational Bayes and Beyond: Bayesian Inference for Big Data, Tamara Broderick. https://www.youtube.com/watch?v=DYRK0-_K2UU
• Variational Inference: A Review for Statisticians, David M. Blei, Alp Kucukelbir, Jon D. McAuliffe. https://arxiv.org/abs/1601.00670

Normalizing Flows

Journal Club

petit journal club