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 ;)

A game theoretic approach to explain the output of any machine learning model.

The fastai book, published as Jupyter Notebooks

Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable…

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…

Code for Machine Learning for Algorithmic Trading, 2nd edition.

Jupyter Interactive Notebook

PRML algorithms implemented in Python

Automatic extraction of relevant features from time series:

Notebooks and code for the book "Introduction to Machine Learning with Python"

Portfolio and risk analytics in Python

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Probabilistic reasoning and statistical analysis in TensorFlow

Performance analysis of predictive (alpha) stock factors

Bayesian optimization in PyTorch

Productivity Tools for Plotly + Pandas

Quantitative research and educational materials

IPython Parallel: Interactive Parallel Computing in Python

A python tutorial on bayesian modeling techniques (PyMC3)

An interactive data visualization tool which brings matplotlib graphics to the browser using D3.

A clear, concise, simple yet powerful and efficient API for deep learning.

Notebooks about Bayesian methods for machine learning

Tutorial on scikit-learn and IPython for parallel machine learning

Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

IPython kernel for Torch with visualization and plotting

Bayesian Data Analysis demos for Python

[RETIRED] See Voilà as a supported replacement

Gaussian Process Optimization using GPy

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