1.lyogavin / airllm
AirLLM 2.8T LLM inference with single 4GB GPU
- chinese-nlp
- finetune
- generative-ai
- instruct-gpt
- instruction-set
- llama
- +7 more
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AirLLM 2.8T LLM inference with single 4GB GPU
12 Weeks, 24 Lessons, AI for All!
18 Lessons to Get Started Building AI Agents
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
21 Lessons, Get Started Building with Generative AI
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. Join the course here 👇🏼
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Examples and guides for using the OpenAI API
This is a repo with links to everything you'd ever want to learn about data engineering
Anthropic's Interactive Prompt Engineering Tutorial
This repository is maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), AI security, vulnerability research, exploit development, reverse engineering, and more. 🔥 Also check: https://hackertraining.org
✅(已完结)超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
One has no future if one couldn't teach themself.
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
10 Weeks, 20 Lessons, Data Science for All!
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
Learn how to develop, deploy and iterate on production-grade ML applications.
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调(全参数/Lora)、部署国内外开源大模型(LLM)/多模态大模型(MLLM)教程
Python Data Science Handbook: full text in Jupyter Notebooks
Anthropic's educational courses
FinRL®: Financial Reinforcement Learning. 🔥
Learn ML engineering for free in 4 months! Register here 👇🏼
[🔥updating ...] AI 自动量化交易机器人(完全本地部署) AI-powered Quantitative Investment Research Platform. 📃 online docs: https://ufund-me.github.io/Qbot ✨ :news: qbot-mini: https://github.com/Charmve/iQuant
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑🔬
本项目是一个面向小白开发者的大模型应用开发教程,在线阅读地址:https://datawhalechina.github.io/llm-universe/
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Reference PyTorch implementation and models for DINOv3
Google Research
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Python training for business analysts and traders
Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
A latent text-to-image diffusion model
A simple screen parsing tool towards pure vision based GUI agent
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
Examples and guides for using the Gemini API
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
Companion webpage to the book "Mathematics For Machine Learning"
A High-Quality Real Time Upscaler for Anime Video
LangGPT: Empowering everyone to become a prompt expert! 🚀 📌 结构化提示词(Structured Prompt)提出者 📌 元提示词(Meta-Prompt)发起者 📌 最流行的提示词落地范式 | Language of GPT The pioneering framework for structured & meta-prompt design 10,000+ ⭐ | Battle-tested by thousands of users worldwide Created by 云中江树
Your new Mentor for Data Science E-Learning.
PyTorch code and models for the DINOv2 self-supervised learning method.
Practice your pandas skills!
No description provided by the repository host.
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.
Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
The fastai book, published as Jupyter Notebooks
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
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.
A game theoretic approach to explain the output of any machine learning model.
深度学习入门教程, 优秀文章, Deep Learning Tutorial
Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you.
Course Files for Complete Python 3 Bootcamp Course on Udemy
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
A guidance language for controlling large language models.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Learn OpenCV : C++ and Python Examples
《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
This repository contains implementations and illustrative code to accompany DeepMind publications
Get started with building Fullstack Agents using Gemini 2.5 and LangGraph
12 Weeks, 24 Lessons, IoT for All!
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
The fastai deep learning library
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
💿 Free software that works great, and also happens to be open-source Python.
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
Lists of company wise questions available on leetcode premium. Every csv file in the companies directory corresponds to a list of questions on leetcode for a specific company based on the leetcode company tags. Updated as of May, 2022.
Automatic extraction of relevant features from time series:
No description provided by the repository host.
No description provided by the repository host.
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
Performance analysis of predictive (alpha) stock factors
YSDA course in Natural Language Processing
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.