As deep learning is a relatively recent topic, there are no published textbooks, although there are several books being prepared for publication in the near future. We will rely on a number of tutorial papers as well as research papers for background reading. A few of them are listed on this page.

1. • Learning Deep Architectures for AI, by Yoshua Bengio, Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1-127, 2009.
   

2. • Learning representations by backpropagating errors, by David Rumelhart, Geoffrey Hinton, and Ronald Williams, Nature, Springervol. 323, October 1986.
   

3. • Reducing the dimensionality of data with neural networks, by Geoffrey Hinton and Ruslan Salakhutdinov, Science, vol. 313, July 2006.
   

4. • Extracting and Composing Robust Features with Denoising Autoencoders, by Pascal Vincent et al., ICML 2008 .
   

5. • Human level control through deep reinforcement learning, Volodymyr Mnih et al., Nature, vol. 518, Feb 2015.
   

6. • Learning with Pseudo-Ensembles, by Philip Bachman, Ouasis Alsharif, and Doina Precup, NIPS 2014.