Deep Learning for humans
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Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
Streamlit — A faster way to build and share data apps.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Making large AI models cheaper, faster and more accessible
💫 Industrial-strength Natural Language Processing (NLP) in Python
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.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Code and documentation to train Stanford's Alpaca models, and generate the data.
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
Best Practices on Recommendation Systems
pix2tex: Using a ViT to convert images of equations into LaTeX code.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
A MNIST-like fashion product database. Benchmark :point_down:
pix2code: Generating Code from a Graphical User Interface Screenshot
Low-code framework for building custom LLMs, neural networks, and other AI models
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
The Triton Inference Server provides an optimized cloud and edge inferencing solution.