Popular GitHub Repositories for Augmentation Methods

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

Image augmentation for machine learning experiments.

? LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

A curated list of awesome self-supervised methods

A system for quickly generating training data with weak supervision

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images.

Image augmentation library in Python for machine learning.

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

A collection of important graph embedding, classification and representation learning papers with implementations.

Data augmentation for NLP

Probabilistic reasoning and statistical analysis in TensorFlow

Text recognition (optical character recognition) with deep learning methods, ICCV 2019

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

??High-Performance Face Recognition Library on PaddlePaddle & PyTorch??

TextAttack ? is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

Learning in infinite dimension with neural operators.

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！