Lastly updated on 2018-12-13
Papers
- ODE solvers – A radical approach to Deep Learning – link
Courses
- “Machine Learning” with Andew NG, provided by Coursera / Stanford U – CS229. It is like Machine Learning 101. It helps you get the basics right – regressions, learnable params, classification, neural nets, validation, how to construct models, etc. [videos] [slides]
- “Convolutional Neural Networks for Visual Recognition” with Andrej Karpathy, Justin Johnson, and Fei-Fei Li, provided by Stanford U – CS231n [videos] [slides]
- “Deep Learning” with Yann LeCun, provided by College De France [videos] [slides]
- “Probabilistic Graphical Models” with Daphne Koller, provided by Coursera / Stanford U – CS228 [videos] [slides]
- “Neural Networks for Machine Learning” with Geoffrey Hinton, provided by Coursera / University of Toronto [videos] [slides]
- “Representation Learning and Deep Learning” with Yoshua Bengio, provided by University of Montreal – IFT6266 H-2016 [videos] [slides]
- “Machine Learning” with Nando De Freitas, provided by Oxford U [videos] [slides]
- “Neural networks” with Hugo Larochele, provided by Universit? de Sherbrooke [videos] [slides]
- “Tutorial on Deep Learning” with Ruslan Salakhutdinov, provided by Simons Institute, Berkeley U [videos] [slides]
- “Deep Learning for Natural Language Processing” with
- “Tensorflow for Deep Learning” provided by Stanford University [web], [slides], [video]
Tutorials
Books
- “Deep Learning” by Ian Goodfellow and Yoshua Bengio and Aaron Courville [pdf] [paper]
- “Pattern Recognition and Machine Learning” by Christopher Bishop [paper]
Blogs
Tools
- pytorch + caffe2, tensorflow + keras
- onnx, gluon, mxnet
External lists
- “Most cited papers since 2012” by Terry Taewoong Um [web]
- “Deep Learning for NLP resources” by Andrew Thomas [web]