PyTorch Tutorial for Deep Learning Researchers
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Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
State-of-the-art 2D and 3D Face Analysis Project
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
The fastai deep learning library
GUI for a Vocal Remover that uses Deep Neural Networks.
deep learning for image processing including classification and object-detection etc.
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
Image-to-Image Translation in PyTorch
Graph Neural Network Library for PyTorch
ncnn is a high-performance neural network inference framework optimized for the mobile platform
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
Open standard for machine learning interoperability
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
Machine Learning Engineering Open Book
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
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