Learning Materials

Here is a list for learning various topics of machine learning, computational neuroscience, and biological neuroscience.

Deep Learning

Neuromatch Academy: Deep Learning. https://deeplearning.neuromatch.io/tutorials/intro.html

Dive into Deep Learning. https://d2l.ai

Deep Learning. https://www.deeplearningbook.org

Machine Learning

Pattern Recognition and Machine Learning by Bishop. Python codes implementing algorithms. https://github.com/ctgk/PRML

Machine Learning - An Algorithmic Perspective. https://homepages.ecs.vuw.ac.nz/~marslast/MLbook.html

Probabilistic machine learning: a book series by Kevin Murphy. https://github.com/probml/pml-book

Computational Neuroscience

Neuromatch Academy: Computational Neuroscience. https://compneuro.neuromatch.io/tutorials/intro.html

Neuronal Dynamics - From single neurons to networks and models of cognition. https://neuronaldynamics.epfl.ch

Spiking Neuron Models - Single Neurons, Populations, Plasticity. https://lcnwww.epfl.ch/gerstner/SPNM/SPNM.html

Dynamical Systems in Neuroscience - The Geometry of Excitability and Bursting. https://www.izhikevich.org/publications/dsn/index.htm

Introduction to neural computation. https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018

Neuroscience

WEBVISION - The Organization of the Retina and Visual System. https://webvision.med.utah.edu

Neuroscience Online. https://nba.uth.tmc.edu/neuroscience/m/index.htm