Learn how to design, develop, deploy and iterate on production-grade ML applications.

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 ;)

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

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…

A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸

This repository contains the source code for the paper First Order Motion Model for Image Animation

FinRL®: Financial Reinforcement Learning. 🔥

Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! W…

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

An annotated implementation of the Transformer paper.

Portfolio and risk analytics in Python

Silero Models: pre-trained text-to-speech models made embarrassingly simple

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…

Text and supporting code for Think Stats, 2nd Edition

Collection of useful data science topics along with articles, videos, and code

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's …

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Structured state space sequence models

🪼 a python library for doing approximate and phonetic matching of strings.

PyMC educational resources

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Single-document unsupervised keyword extraction

DeepStream SDK Python bindings and sample applications

My solution to the book A Collection of Data Science Take-Home Challenges

Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).