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An opinionated list of awesome Python frameworks, libraries, software and resources.
All Algorithms implemented in Python
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Models and examples built with TensorFlow
A curated list of awesome Machine Learning frameworks, libraries and software.
scikit-learn: machine learning in Python
Clone a voice in 5 seconds to generate arbitrary speech in real-time
TensorFlow code and pre-trained models for BERT
A toolkit for developing and comparing reinforcement learning algorithms.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
💫 Industrial-strength Natural Language Processing (NLP) in Python
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Deezer source separation library including pretrained models.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Minimal examples of data structures and algorithms in Python
Python sample codes for robotics algorithms.
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Graph Neural Network Library for PyTorch
☁️ Build multimodal AI applications with cloud-native stack
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Open standard for machine learning interoperability
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Python package built to ease deep learning on graph, on top of existing DL frameworks.
