Python Data Science Handbook: full text in Jupyter Notebooks

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Python programs, usually short, of considerable difficulty, to perfect particular skills.

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

The "Python Machine Learning (1st edition)" book code repository and info resource

PRML algorithms implemented in Python

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

Public facing notes page

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

Multi-Joint dynamics with Contact. A general purpose physics simulator.

Deep Learning Specialization by Andrew Ng on Coursera.

this repository accompanies the book "Grokking Deep Learning"

The "Python Machine Learning (2nd edition)" book code repository and info resource

Python code for "Probabilistic Machine learning" book by Kevin Murphy

A course in reinforcement learning in the wild

"Probabilistic Machine Learning" - a book series by Kevin Murphy

Repo for the Deep Reinforcement Learning Nanodegree program

Useful functions, tutorials, and other Python-related things

A python tutorial on bayesian modeling techniques (PyMC3)

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath

The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML

State Space Models library in JAX

A collection of Bayesian data analysis recipes using PyMC3