Peer-to-peer hypermedia protocol

Papers with code. Sorted by stars. Updated weekly.

Code for the paper "Language Models are Unsupervised Multitask Learners"

Natural Language Processing Tutorial for Deep Learning Researchers

Scan, index, and archive all of your paper documents

A curated list to learn about distributed systems

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

Visual Tracking Paper List

CVPR 2020 论文开源项目合集

Code for the paper "Jukebox: A Generative Model for Music"

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"

[CVPR2020] Adversarial Latent Autoencoders

Deep Learning and deep reinforcement learning research papers and some codes

Summaries of machine learning papers

Paper is a fast NoSQL-like storage for Java/Kotlin objects on Android with automatic schema migration support.

A collection of AWESOME things about domian adaptation

Code for the paper "Improved Techniques for Training GANs"

Image Deblurring using Generative Adversarial Networks

Code for the paper "PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications"

DeepMind's Tacotron-2 Tensorflow implementation

Code and model for the paper "Improving Language Understanding by Generative Pre-Training"

Code for "Learning to Generate Reviews and Discovering Sentiment"

Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"

Minimal and formal résumé (CV) website template for print, mobile, and desktop. https://bit.ly/ur_demo

📄 🇨🇳 📃 论文阅读笔记（分布式，虚拟化，机器学习）Papers Notebook (Distributed System, Virtualization, Machine Learning), created by @gaocegege

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

Code for the paper "Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents"

📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.