pytorch

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

Data manipulation and transformation for audio signal processing, powered by PyTorch

The author's officially unofficial PyTorch BigGAN implementation.

AI comic and manga translator app/browser extension for automatically translating comics, manga, manhwa, BDs, fumetti, and more in multiple languages and formats (Images, PDF, EPUB, CBR, CBZ etc).

C++ library based on tensorrt integration

✨Fast Coreference Resolution in spaCy with Neural Networks

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

AIGC-interview/CV-interview/LLMs-interview面试问题与答案集合仓，同时包含工作和科研过程中的新想法、新问题、新资源与新项目

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Implementation of papers in 100 lines of code.

A computer vision closed-loop learning platform where code can be run interactively online. 学习闭环《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（持续更新中 ...） 📘 在线电子书 https://charmve.github.io/computer-vision-in-action/ 👇项目主页

Multilingual Automatic Speech Recognition with word-level timestamps and confidence

A Unified Library for Parameter-Efficient and Modular Transfer Learning

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

Enabling PyTorch on XLA Devices (e.g. Google TPU)

We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台，我们欢迎开源爱好者发起任何有意义的pr！

List of papers, code and experiments using deep learning for time series forecasting

Images to inference with no labeling (use foundation models to train supervised models).

Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.