Qihong Lu

Online courses

• Theories of Deep Learning [lectures], Hatef Monajemi, et al., Stanford University
• ConvNet for visual recognition, Fei-Fei Li, et al., Stanford University
• Deep Learning, Nando de Freitas, Oxford University
• Deep Learning, Amini et al., MIT
• Practical Deep Learning For Coders, fast.ai
• Machine Learning Crash Course, Google
• Intro to neural nets, Hugo Larochelle, Université de Sherbrooke

• Reinforcement Learning, David Silver, University College London
• Deep Reinforcement Learning, Sergey Levine, UC Berkeley
• Deep RL Bootcamp, [lectures, labs], UC Berkeley

• Intro to Machine Learning, Tom Mitchell, CMU
• Convex Optimization A B , Stephen P. Boyd, Stanford
• Bayesian Statistics, Nicholas Zabaras, University of Notre Dame
• Statistical Learning Theory and Applications [lectures], Tomaso Poggio, et al., MIT
• Statistical Machine Learning, Ryan Tibshirani, Larry Wasserman, CMU
• Machine Learning the Future, Cornell Tech and Cornell

Books

• The PDP handbook, Jay McClelland
• Computational cognitive neuroscience, Randall O'Reilly
• Deep learning, [videos], Ian Goodfellow, Yoshua Bengio and Aaron Courville
• Intro to reinforcement learning, Richard Sutton and Andrew Barto
• Probabilistic Models of Cognition, Noah D. Goodman & Joshua B. Tenenbaum
• Intro stats, [git repo], Russell A. Poldrack

Other resources

• Cognitive modeling: classic papers
• Deep learning: recent papers, useful repos, deep RL
• Bayesian: Reading list, Bayesian Nonparametrics
• Other stuff: fMRI Bootcamp, nancys braintalks The EEGLAB Tutorial,
• Blogs: Niko Kriegeskorte, Distill, Christopher Olah, Andrej Karpathy

Frameworks

• BrainIAK: Brain Imaging Analysis Kit
• OpenAI Gym
• PyTorch
• Tensorflow
  • Tensorflow for Deep Learning Research: a course at Stanford
  • Keras: a high level API built on top of Tensorflow.
  • Neural Network Playground: Play with a feed-forward network!
  • Some models: Model Zoo, GANs, RBMs
  • more stuff...

Other stuff

• Fundamentals of Data Visualization