[ICML 2020] On the Noisy Gradient Descent that Generalizes as SGD

PyTorch Implementation of Momentum-Based Policy Gradient Methods

Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

这是一个vue仓库

微信中的知乎--微信小程序 demo // Zhihu in Wechat

Promise based HTTP client for the browser and node.js

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

A toolkit for developing and comparing reinforcement learning algorithms.

📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, in…

📚 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

🏦 银行笔试面试经验分享及资料分享（help you pass the bank interview, and get a amazing bank offer!）

source code for the paper "Policy Search by Target Distribution Learning for Continuous Control"

java项目实战练习

Reinforcement learning with the implementation of the emphatic TD of Sutton & al. (2015)

Experiment code for our project on actor-critic algorithms with emphatic weightings.

The most cited deep learning papers

Reinforcement learning resources curated

 Now we have become very big, Different from the original idea. Collect premium software in various categories.

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

莫烦Python Website source code

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

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

牛客网刷题记录

CS231课程笔记翻译 https://zhuanlan.zhihu.com/intelligentunit

CNN-RNN中文文本分类，基于tensorflow

BiLstm+CNN+CRF 法律文档（合同类案件）领域分词（100篇标注样本）

deep-learning-practice