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AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Graph Neural Network Library for PyTorch
PyTorch implementations of Generative Adversarial Networks.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
A Python implementation of global optimization with gaussian processes.
PyTorch implementations of deep reinforcement learning algorithms and environments
An Open-Source Package for Knowledge Embedding (KE)
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKT…
A highly efficient implementation of Gaussian Processes in PyTorch
Generate embeddings from large-scale graph-structured data.
Code for visualizing the loss landscape of neural nets
PyTorch original implementation of Cross-lingual Language Model Pretraining.
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
Machine learning metrics for distributed, scalable PyTorch applications.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Benchmark datasets, data loaders, and evaluators for graph machine learning
An Open-Source Package for Network Embedding (NE)
A TensorFlow implementation of Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
Pytorch implementation of Graph U-Nets (ICML19)
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
Code for PaperRobot: Incremental Draft Generation of Scientific Ideas
Framework for evaluating Graph Neural Network models on semi-supervised node classification task