zxtxjtu

zxtxjtu's Stars

• Snailclimb/JavaGuide

  「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！

  Language:Java147k4.5k1k45.6k

• labuladong/fucking-algorithm

  刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

  Language:Markdown126k2.3k84623.2k

• openai/openai-cookbook

  Examples and guides for using the OpenAI API

  Language:MDX59.5k8744669.5k

• scutan90/DeepLearning-500-questions

  深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06

  Language:JavaScript54.7k2.2k18915.9k

• geekxh/hello-algorithm

  🌍 针对小白的算法训练 | 包括四部分：①.大厂面经 ②.力扣图解 ③.千本开源电子书 ④.百张技术思维导图（项目花了上百小时，希望可以点 star 支持，🌹感谢~）推荐免费ChatGPT使用网站

  Language:Java35.2k658426.5k

• pyg-team/pytorch_geometric

  Graph Neural Network Library for PyTorch

  Language:Python21.3k2523.5k3.6k

• rlabbe/Kalman-and-Bayesian-Filters-in-Python

  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 filters, and more. All exercises include solutions.

  Language:Jupyter Notebook16.6k4613254.2k

• openai/baselines

  OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

  Language:Python15.8k6478504.9k

• dair-ai/ml-visuals

  🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

  13.5k116491.4k

• jindongwang/transferlearning

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

  Language:Python13.4k3393393.8k

• openai/spinningup

  An educational resource to help anyone learn deep reinforcement learning.

  Language:Python10.1k2282832.2k

• wolverinn/Waking-Up

  计算机基础（计算机网络/操作系统/数据库/Git...）面试问题全面总结，包含详细的follow-up question以及答案；全部采用【问题+追问+答案】的形式，即拿即用，直击互联网大厂面试；可用于模拟面试、面试前复习、短期内快速备战面试...

  9.8k166341.8k

• naganandy/graph-based-deep-learning-literature

  links to conference publications in graph-based deep learning

  Language:Jupyter Notebook4.8k25214774

• LantaoYu/MARL-Papers

  Paper list of multi-agent reinforcement learning (MARL)

  4k2399728

• DeepGraphLearning/LiteratureDL4Graph

  A comprehensive collection of recent papers on graph deep learning

  3.1k1919564

• openai/multiagent-particle-envs

  Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

  Language:Python2.4k17683783

• oxwhirl/pymarl

  Python Multi-Agent Reinforcement Learning framework

  Language:Python1.9k29130384

• nnzhan/MTGNN

  Language:Python8001038221

• acbull/pyHGT

  Code for "Heterogeneous Graph Transformer" (WWW'20), which is based on pytorch_geometric

  Language:Python7881453162

• tinyzqh/light_mappo

  Lightweight version of MAPPO to help you quickly migrate to your local environment.

  Language:Python49212279

• wenqifan03/GraphRec-WWW19

  Graph Neural Networks for Social Recommendation, WWW'19

  Language:Python36180109

• xchadesi/GraphNeuralNetwork

  The learning of the GraphNeuralNetwork

  1516041

• chauncygu/Multi-Agent-Constrained-Policy-Optimisation

  Multi-Agent Constrained Policy Optimisation (MACPO; MAPPO-L).

  Language:Python14321024

• spindro/GINN

  Graph Imputation Neural Network

  Language:Python774826

• jiang719/road-network-predictability

  Language:Jupyter Notebook703111

• caltech-netlab/acnportal-experiments

  Case studies to demonstrate the use of ACN-Sim.

  Language:Dockerfile31537

• MingxiLii/LocaleGN

  few-shot models for short-term traffic prediction

  Language:Python16215

• zxtxjtu/graph_nets

  Build Graph Nets in Tensorflow

  Language:Python1000

• zxtxjtu/pytorch_geometric

  Geometric Deep Learning Extension Library for PyTorch

  Language:Python1000

• zxtxjtu/SEAL

  SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). "M. Zhang, Y. Chen, Link Prediction Based on Graph Neural Networks, NIPS 2018 spotlight".

  Language:C1000