Topics covered include:
- 5 General overview papers (Bengio, Schmidhuber, Graves ….)
- 12 Foundation Theory and Motivation papers (Bingio, Hinton, LeCun, ….)
- 7 Computer Vision papers (Deep Convolutional Networks, Hierarchical Features, Practical Applications)
- 5 Papers on Natural Language Processing and Speech (Recursive Auto-encoders, Bidirectional Long Short Term Memory)
- 15 Papers on Unsupervised Feature Learning (Deep Boltzmann Machines, Autoencoders, …)
- And about 40 papers under the headings: Disentangling Factors and Varitions with Depth, Transfer Learning and domain adaptation, Practical Tricks and Guides, Sparse Coding, Classification, Large Scale Deep Learning, Recurrent Networks, Hyper Parameters, Miscellaneous, and Optimization.
They conclude their list with a list of three other machine learning reading lists and three other links to deep learning tutorials.