Awesome Python

An Open Source Machine Learning Framework for Everyone

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Deep Learning for humans

Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

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!

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.

💫 Industrial-strength Natural Language Processing (NLP) in Python

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

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

1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

🐍 Geometric Computer Vision Library for Spatial AI

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

BirdNET analyzer for scientific audio data processing.

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.