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An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
A Python package for tensor computation with GPU acceleration and dynamic neural networks built on a tape-based autograd system.
An open-source library with over 2500 optimized algorithms for real-time computer vision and machine learning.
Open Source Computer Vision Library providing real-time image processing and AI capabilities.
A comprehensive open-source guide covering prompt engineering techniques, papers, notebooks, and resources for LLMs, RAG, and AI agents.
A repository of state-of-the-art model implementations and examples built with TensorFlow, demonstrating best practices for research and production.
A repository of state-of-the-art model implementations and examples built with TensorFlow, demonstrating best practices for machine learning.
A curated list of awesome machine learning frameworks, libraries, and software organized by programming language.
A curated list of awesome machine learning frameworks, libraries, and software organized by programming language.
A latent text-to-image diffusion model that generates detailed images from text prompts, running on GPUs with at least 10GB VRAM.
A collection of 60+ annotated PyTorch implementations of deep learning papers with side-by-side explanatory notes.
A multi-backend deep learning framework that enables effortless model development across JAX, TensorFlow, PyTorch, and OpenVINO.
A cutting-edge framework for training and deploying state-of-the-art YOLO models for object detection, segmentation, classification, and pose estimation.
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
A simple Python library and CLI tool for facial recognition, detection, and feature manipulation using dlib's deep learning models.
A deep learning toolkit for Text-to-Speech generation with pretrained models in over 1100 languages and tools for training.
Transform Python scripts into interactive web apps for data dashboards, reports, and chat apps in minutes.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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 machine learning web apps and demos in Python with minimal code.
A comprehensive collection of TensorFlow tutorials and examples for beginners, covering both TF v1 and v2 with clear explanations.
A unified deep learning system for efficient large-scale model training and inference with advanced parallelism strategies.
A curated reading roadmap of foundational and state-of-the-art deep learning papers for newcomers and researchers.
Cross-platform framework for building customizable on-device machine learning pipelines for live and streaming media.
A comprehensive collection of PyTorch image models, layers, utilities, and training scripts for computer vision research and applications.
A PyTorch-based platform for state-of-the-art object detection, segmentation, and visual recognition tasks.
A modular PyTorch library for state-of-the-art diffusion models to generate images, audio, and 3D molecular structures.
A fast open framework for deep learning with a focus on expression, speed, and modularity.
Real-time multi-person keypoint detection library for body, face, hands, and foot estimation.
A visualizer for neural network, deep learning, and machine learning models across multiple frameworks.
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility and transparency.
A collection of concise PyTorch tutorials for deep learning researchers, with most models implemented in under 30 lines of code.
A deep learning framework to pretrain and finetune any AI model on any hardware with zero code changes.
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