Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

PyMC educational resources

Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

Gaussian processes in TensorFlow

A python tutorial on bayesian modeling techniques (PyMC3)

Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.

How to do Bayesian statistical modelling using numpy and PyMC3

Code for modelling estimated deaths and cases for COVID19.

RStan, the R interface to Stan

Bayesian inference with probabilistic programming.

Statistical package in Python based on Pandas

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

rstanarm R package for Bayesian applied regression modeling

A collection of Bayesian data analysis recipes using PyMC3

Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

R Markdown notes for Peter D. Hoff, "A First Course in Bayesian Statistical Methods"