hackthemarket/gym-trading

lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.

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 goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Some ipython notebooks implementing AI algorithms

:musical_note: Algorithms written in different programming languages - https://zoranpandovski.github.io/al-go-rithms/

Research Framework for Single and Multi-Players Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson…) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on

Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

A driving dataset for the development and validation of fused pose estimators and mapping algorithms

Python implementation of algorithms from Russell And Norvig’s “Artificial Intelligence - A Modern Approach”

Files for Udemy Course on Algorithms and Data Structures