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

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.

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.

A python tutorial on bayesian modeling techniques (PyMC3)

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

Bayesian inference with probabilistic programming.

PyMC educational resources

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

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

Gaussian processes in TensorFlow

Statistical package in Python based on Pandas

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

BAyesian Model-Building Interface (Bambi) in Python.

RStan, the R interface to Stan

Code for modelling estimated deaths and cases for COVID19.

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

How to do Bayesian statistical modelling using numpy and PyMC3

A collection of Bayesian data analysis recipes using PyMC3

A Python library for amortized Bayesian workflows using generative neural networks.

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.