Deep Learning Study Group

Video Lectures:

• Richard Socher, CS224d. Stanford University, 2015.
• Geoffrey Hinton, Neural Networks for Machine Learning. Coursera, 2012.

Textbooks and Chapters:

• Yoshua Bengio, Deep Learning. Book in preparation for MIT Press, 2015.
• Yoshua Bengio, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, Vol. 2, No. 1 (2009) 1–127. 
• Hal Daumé III, A Course in Machine Learning, (preprint), 2015.

Surveys and Collections: 

• Jürgen Schmidhuber, Deep Learning in Neural Networks: An Overview. Neural Networks, Volume 61, January 2015, Pages 85-117.
• Jürgen Schmidhuber, Literature Recommendations for New Researchers. Reddit.com, 2015.
• Yoshua Bengio et al., Deep Learning Tutorials. Deeplearning.net, 2015.
• Wojciech Samek, Hot Topics in Machine Learning: Deep Learning (Reading Material). TU Berlin, 2015.
• Christopher D. Manning, Last Words: Computational Linguistics and Deep Learning. COLI, MIT Press, 2015.
• Myungsub Choi, Jiwon Kim, Awesome Recurrent Neural Networks: A curated list of resources dedicated to recurrent neural networks, github.com.

Articles and Applications:

• Richard Socher et al., Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. EMNLP'13.
• Giuseppe Attardi, DeepNL: a Deep Learning NLP pipeline. NAACL'15.
• Andrej Karpathy, The Unreasonable Effectiveness of Recurrent Neural Networks. 2015.

Software: 

• Theano and pylearn2 (Python)
• Torch7 (Lua)
• Caffe (C++)
• Tensorflow (Python/C++)
• Keras (Python)