Qihong Lu
Resources
Online courses
Deep learning:- 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
Other resources
- Deep learning: recent papers, useful repos, deep RL
- Bayesian: Reading list, Bayesian Nonparametrics
- Other stuff: fMRI Bootcamp, 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