[11:1] Deep Learning §14 Autoencoders I. Goodfellow, Y. Benghio, A. Courville – MIT Press 2016
[11:2] Introduction to Autoencoders, P. Galeone - 2016 - https://pgaleone.eu/neural-networks/2016/11/18/introduction-to-autoencoders/
[11:3] An introduction to Restricted Boltzmann machines A Fisher, C. Igel – University of Copenhagen - http://image.diku.dk/igel/paper/AItRBM-proof.pdf
[11:4] Machine Learning: A Probabilistic Perspective §27.7 Restricted Boltzmann Machines, K Murphy - MIT Press 2012
[11:5] Deep Learning §20.3 Deep Belief Networks, I. Goodfellow, Y. Benghio, A. Courville – MIT Press 2016
[11:6] Deep Learning 18.2 Stochastic Maximum Likelihood and Contrastive Divergence I. Goodfellow, Y. Benghio, A. Courville -MIT Press 2016
[11:7] Markov Chain Monte Carlo Wikipedia - https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo
[11:8] Notes on Contrastive Divergence, O. Woodford - http://www.robots.ox.ac.uk/~ojw/files/NotesOnCD.pdf
[11:9] Convergence Analysis of the Contrastive Divergence Algorithm, X. Ma, X. Wang – Hindawi 2015 - https://www.hindawi.com/journals/mpe/2015/350102/
[11:10] An Introduction to Convolution Neural Network – Stanford University 2013 - http://white.stanford.edu/teach/index.php/An_Introduction_to_Convolutional_Neural_Networks
[11:11] Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech Recognition, O. Abdel-Hamid, L. Deng, D. Yu – Microsoft Research, Interspeech 2013 - http://research.microsoft.com/pubs/200804/CNN-Interspeech2013_pub.pdf