Popular GitHub Repositories for Useful

<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

An AI agent that writes (actually useful) code for you

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

200+ detailed flashcards useful for reviewing topics in machine learning, computer vision, and computer science.

A Python library for audio data augmentation. Useful for making audio ML models work well in the real world, not just in the lab.

Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier

Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

A curated list of useful resources that cover Offensive AI.

Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.

??? 개발자를 위한 북마크 모음집

Lightweight, useful implementation of conformal prediction on real data.

Useful GPT Prompts

A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.

The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

??️ Generate photo-realistic textures based on source images or (soon) PBR materials. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture.

Useful links of different content related to AI, Computer Vision, and Robotics.

Streaming highlighting with Shiki. Useful for highlighting text streams like LLM outputs.

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).