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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 ;)
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
A game theoretic approach to explain the output of any machine learning model.
Data and code behind the articles and graphics at FiveThirtyEight
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…
Natural Language Processing Tutorial for Deep Learning Researchers
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
Python toolkit for quantitative finance
Portfolio and risk analytics in Python
"Probabilistic Machine Learning" - a book series by Kevin Murphy
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Probabilistic reasoning and statistical analysis in TensorFlow
A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/
Performance analysis of predictive (alpha) stock factors
Beaker Extensions for Jupyter Notebook
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Quantitative research and educational materials
PyMC educational resources
Notebooks about Bayesian methods for machine learning
Data and methodology for the Big Mac index
A small library for automatically adjustment of text position in matplotlib plots to minimize overlaps.