Here’re some machine learning and computational modeling resources for researchers in psychology and neuroscience.

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
• Intro to neural nets, Hugo Larochelle, Université de Sherbrooke Deep reinforcement learning:
• Reinforcement Learning, David Silver, University College London
• Deep Reinforcement Learning, Sergey Levine, UC Berkeley
• Deep RL Bootcamp, [lectures, labs], UC Berkeley Fundations:
• Intro to Machine Learning, Tom Mitchell, CMU
• 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

Readings:

• Deep learning: recent papers, useful repos, deep RL
• Bayesian: Bayesian methods, Bayesian Nonparametrics
• Other stuff: The EEGLAB Tutorial
• Blogs: Niko Kriegeskorte, Distill, Christopher Olah, Andrej Karpathy, Shakir Mohamed

Frameworks

Tensorflow: a reverse mode auto-differentiation library in Python

• Keras: a high level API built on top of Tensorflow.
• Neural Network Playground: Play with a feed-forward network!
• Model Zoo: Neural networks implemented in TensorFlow
• Tensorflow for Deep Learning Research: a course at Stanford OpenAI Gym: simulated environment for training RL agents LENS: the light, efficient network simulator. emergent: a comprehensive neural network simulator. BrainIAK: Brain Imaging Analysis Kit

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