Deep and online learning with spiking neural networks in Python

Implementation of Expectation Propagation algorithms.

Implementation of Expectation Propagation algorithms.

Implementation of the Expectation Propagation algorithms to perform approximate inference on the benchmark bayesian clutter problem

Neuroscience for machine learners course

Brian is a free, open source simulator for spiking neural networks.

All information of the Neural Data Science with Python course of the Neuroscience Master program at Université Paris Cité.

Notebooks about Bayesian methods for machine learning

⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

PyTorch models for RNNs commonly used in computational neuroscience.

Tutorial codes for modeling brains with neural nets

Tutorial for understanding how to use NEURON simulation environment

Interactive Tutorials on Training Spiking Neural Network With Backprop

This repository serves as a container for material around the Brian simulator, such as presentations and tutorials.

This is now in the docs folder of the main NEURON repository. Make all changes there.

for NEURON tutorial

This is now in the docs folder of the main NEURON repository. Make all changes there.

All information of the Neural Data Science with Python course of the Neuroscience Master program at Université Paris Cité.

Canonical Correlation Analysis, Variational CCA

Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertain…

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

Estimate Gaussian Mixture Model (GMM) with both EM Algorithm and Variational Inference (Variational Bayesian) from scratch

Implementation of Bayesian Logistic Regression using Expectation Propagation for approximate inference

I put some codes of the exercises and contents in Bayesian Data Analysis, Third Edition (BDA3) (Andrew Gelman el al. (2013)).

A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book

PRML algorithms implemented in Python

State Space Models library in JAX

Statistical Rethinking (2nd ed.) with NumPyro