🦜🔗 Build context-aware reasoning 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 collection of various deep learning architectures, models, and tips

Interview = 简历指南 + 算法题 + 八股文 + 源码分析

A course in reinforcement learning in the wild

Pytorch🍊🍉 is delicious, just eat it! 😋😋

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

Simplified implementations of deep learning related works

机器学习算法的公式推导以及numpy实现

EPFL Course - Optimization for Machine Learning - CS-439

Coursera Open Courses from University of Toronto

GNN综述阅读报告

Jupyter notebooks and other materials developed for the Columbia course APMA 4300