Books on Deep Learning
Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, In preparation.
Survey Papers on Deep Learning
Yoshua Bengio, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, 2(1), pp.1-127, 2009.
Yoshua Bengio, Aaron Courville, Pascal Vincent, Representation Learning: A Review and New Perspectives, Arxiv, 2012.
Jurgen Schmidhuber, Deep Learning and Neural Networks: An Overview, arXiv, 2014.
Deep Learning Code Tutorials
The Deep Learning Tutorials are a walk-through with code for several important Deep Architectures (in progress; teaching material for Yoshua Bengio’s IFT6266 course).
Unsupervised Feature and Deep Learning
Stanford’s Unsupervised Feature and Deep Learning tutorials has wiki pages and matlab code examples for several basic concepts and algorithms used for unsupervised feature learning and deep learning.
- Deep Learning Representations
- Deep Learning with Multiplicative Interactions
Geoffrey Hinton’s talk at the Redwood Center for Theoretical Neuroscience (UC Berkeley, March 2010).
- Recent developments on Deep Learning
Geoffrey Hinton’s GoogleTech Talk, March 2010.
- Learning Deep Hierarchies of Representations
A general presentation done by Yoshua Bengio in September 2009, also at Google.
- A New Generation of Neural Networks
Geoffrey Hinton’s December 2007 Google TechTalk.
- Deep Belief Networks
- Training deep networks efficiently
Geoffrey Hinton’s talk at Google about dropout and “Brain, Sex and Machine Learning”.
- Deep Learning and NLP
- Tutorial on Learning Deep Architectures
[LeCun et al 2006]. A Tutorial on Energy-Based Learning, in Bakir et al. (eds) “Predicting Structured Outputs”, MIT Press 2006: a 60-page tutorial on energy-based learning, with an emphasis on structured-output models. The tutorial includes an annotated bibliography of discriminative learning, with a simple view of CRF, maximum-margin Markov nets, and graph transformer networks.
A 2006 Tutorial an Energy-Based Learning given at the 2006 CIAR Summer School: Neural Computation & Adaptive Perception.[Energy-Based Learning: Slides in DjVu (5.2MB), Slides in PDF (18.2MB)] [Deep Learning for Generic Object Recognition:Slides in DjVu (3.8MB), Slides in PDF (11.6MB)]
ECCV 2010 Tutorial
Feature learning for Image Classification (by Kai Yu and Andrew Ng): introducing a paradigm of feature learning from unlabeled images, with an emphasis on applications to supervised image classification.
NIPS 2010 Workshop
Deep Learning and Unsupervised Feature Learning: basic concepts about unsupervised feature learning and deep learning methods with links to papers and code.
Graduate Summer School: Deep Learning, Feature Learning: IPAM summer school about deep learning.
Geoffrey Hinton’s Online Neural networks Course on Coursera.
Deep Learning for Computer Vision and Natural Language Processing Course from University of Columbia by Liangliang Cao and James Fan.