Monday, August 11, 2014

Deep Learning Mathematical Stack (Updated)

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

  1. Markov Network Models
  2. Ising Models
  3. Variational Methods
  4. Hopfield Networks
  5. Boltzmann Machines
  6. Restricted Boltzmann Machines
  7. Deep Learning

Markov Network Models fall under the category of Probabilistic Graphical Models (PGM).

Below are reference

  1. Books
    1. Information Theory, Inference and Learning Algorithms by David J. C. MacKay
    2. Introduction to the Math of Neural Networks Kindle Edition by Jeff Heaton
  2. Videos
    1. Information Theory of Deep Learning. Naftali Tishby - Aug 3, 2017
    2. Mathematics for Deep Learning - Marc Deisenroth - Sep 13, 2017
    3. Tutorial : Mathematics of Deep Learning - Part 1 - ComputerVisionFoundation Videos - Aug 23, 2017



2 comments:

  1. Obtained the above by using the book Information Theory, Inference and Learning Algorithms by David J. C. MacKay

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