JIAYI LIAO's repositories
JiayiLiao.github.io
This is Jiayi Liao's homepage.
prompt-engineering-for-developers
面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版
pytorch-lightning-template
An easy/swift-to-adapt PyTorch-Lighting template. 套壳模板，简单易用，稍改原来Pytorch代码，即可适配Lightning。You can translate your previous Pytorch code much easier using this template, and keep your freedom to edit all the functions as well. Big-project-friendly as well. No need to rewrite your config in hydra.
llama-recipes
Examples and recipes for Llama 2 model
StatisticalLearning_USTC
Statistical Learning course in USTC. 中科大统计学习（刘东）课程复习资料。
RGCL
Ratioanle-aware Graph Contrastive Learning codebase
Cminusf-Compiler
Compiler cousrse (李诚老师编译原理), 2021 fall, USTC
OpenKE
An Open-Source Package for Knowledge Embedding (KE)
USTC_Resources
中科大计算机学院学习资料
Jiayi.github.io
jiayiliao's personal homepage
ogb
Benchmark datasets, data loaders, and evaluators for graph machine learning
BankSystem
Lab for Database course, 2022spring （金培权老师数据库）
Front-End-Notes
web开发笔记
dgl-ke
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
WebInfo
USTC Web 实验 徐童
FedML
A Research-oriented Federated Learning Library. Supporting distributed computing, mobile/IoT on-device training, and standalone simulation. Best Paper Award at NeurIPS 2020 Federated Learning workshop. Join our Slack Community:(https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w)
privacy-preserving-bandits
Privacy-Preserving Bandits (MLSys'20)
FedNewsRec
Data and code of our paper "Privacy-Preserving News Recommendation Model Learning"
bert
TensorFlow code and pre-trained models for BERT
ai-edu
AI education materials for Chinese students, teachers and IT professionals.
qlib
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies.
recommenders
Best Practices on Recommendation Systems