Junliang Li's starred repositories
Awesome-LLM-Long-Context-Modeling
📰 Must-read papers and blogs on LLM based Long Context Modeling 🔥
LLaMA-Factory
Unify Efficient Fine-Tuning of 100+ LLMs
llm-paper-daily
Daily updated LLM papers. 每日更新 LLM 相关的论文，欢迎订阅 👏 喜欢的话动动你的小手 🌟 一个
LeetcodeTop
汇总各大互联网公司容易考察的高频leetcode题🔥
ChatGLM-6B
ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
llm-action
本项目旨在分享大模型相关技术原理以及实战经验。
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
GNNs-for-Link-Prediction
Some GNNs are implemented using PyG for link prediction tasks, including: GCN, GraphSAGE, GAT, Node2Vec、RGCN、HGT and HAN, which will continue to be updated in the future.
pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
ccf-deadlines
⏰ Collaboratively track deadlines of conferences recommended by CCF (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~
blitz-bayesian-deep-learning
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.
GNNs-for-Node-Classification
Some GNNs are implemented using PyG for node classification tasks, including: GCN, GraphSAGE, SGC, GAT, R-GCN and HAN (Heterogeneous Graph Attention Network), which will continue to be updated in the future.
MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
LSTM-IMDB-Classification
Use PyTorch to build an LSTM model for text classification on the IMDB dataset.
LSTM-MultiStep-Forecasting
Implementation of Electric Load Forecasting Based on LSTM (BiLSTM). Including direct-multi-output forecasting, single-step-scrolling forecasting, multi-model-single-step forecasting, multi-model-scrolling forecasting, and seq2seq forecasting.
WeightWatcher
The WeightWatcher tool for predicting the accuracy of Deep Neural Networks
AT89C52-examples
AT89C52单片机实验程序：发光二极管的亮灭、多个发光二极管分组循环交替亮灭、外部中断控制数码管循环显示0~9、定时器控制发光二极管的亮灭+简单输出连续矩形脉冲。