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Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

:memo: An awesome Data Science repository to learn and apply for real world problems.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

[CVPR2023] Blur Interpolation Transformer for Real-World Motion from Blur

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

pix2tex: Using a ViT to convert images of equations into LaTeX code.