Popular GitHub Repositories for Observer Theory

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

Fast and flexible AutoML with learning guarantees.

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

Datasets, tools, and benchmarks for representation learning of code.

Artificial intelligence for the Snake game.

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

List of papers studying machine learning through the lens of category theory

A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond

Source codes for the book "Reinforcement Learning: Theory and Python Implementation"

AI tutor powered by Theory-of-Mind reasoning

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

A research tool for the Iterated Prisoner's Dilemma

Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

Lecture notes on Bayesian deep learning

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"

A complete and open application for automatic backing tracks generation.

Easy whole-brain modeling for computational neuroscientists ??????

Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.

? 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