《动手学大模型Dive into LLMs》系列编程实践教程

大李敖全集 wjm_tcy mdbook repo

为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…

An awesome repository of community-curated applications of ChatGPT and other LLMs in computational biology

a.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

MLNLP: Notes for MIT-Linear-Algebra

MLNLP社区用来帮助大家避免论文投稿小错误的整理仓库。 Paper Writing Tips

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

The Good Research Code Handbook

A curated list of awesome Bioinformatics libraries and software.

A curated list of awesome R packages, frameworks and software.

Implementation of transformers based architecture in PyTorch.

Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

list of efficient attention modules

🔥🔥超过1000本的计算机经典书籍、个人笔记资料以及本人在各平台发表文章中所涉及的资源等。书籍资源包括C/C++、Java、Python、Go语言、数据结构与算法、操作系统、后端架构、计算机系统知识、数据库、计算机网络、设计模式、前端、汇编以及校招社招各种面经~

Mathematical derivation and pure Python code implementation of machine learning algorithms.

250+ Xshell Color Schemes

🔥 经典编程书籍大全，涵盖：计算机系统与网络、系统架构、算法与数据结构、前端开发、后端开发、移动开发、数据库、测试、项目与团队、程序员职业修炼、求职面试等

Roadmap to becoming an Artificial Intelligence Expert in 2022

PRML algorithms implemented in Python

wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

周志华《机器学习》手推笔记

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

📄 Awesome CV is LaTeX template for your outstanding job application

This is the repository containing the solution of the homework for the CS224W course at Stanford: Machine Learning with Graphs

《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版