Deep learning seems to be a hot topic these days but I haven't been able to find a reference which states the mathematical stack needed to understand it. Below is my attempt at creating one
- Markov Network Models
- Ising Models
- Variational Methods
- Hopfield Networks
- Boltzmann Machines
- Restricted Boltzmann Machines
- Deep Learning
Markov Network Models fall under the category of Probabilistic Graphical Models (PGM).
Below are reference
- Books
- Information Theory, Inference and Learning Algorithms by David J. C. MacKay
- Introduction to the Math of Neural Networks Kindle Edition by Jeff Heaton
- Videos
- Information Theory of Deep Learning. Naftali Tishby - Aug 3, 2017
- Mathematics for Deep Learning - Marc Deisenroth - Sep 13, 2017
- Tutorial : Mathematics of Deep Learning - Part 1 - ComputerVisionFoundation Videos - Aug 23, 2017
Obtained the above by using the book Information Theory, Inference and Learning Algorithms by David J. C. MacKay
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