#deep-learning

Cross-platform, customizable ML solutions for live and streaming media.

Collection of eclectic utils for python.

A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.

BirdNET analyzer for scientific audio data processing.

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.

Statistical Machine Intelligence & Learning Engine

The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai

Next Generation Machine Learning, Statistics and Deep Learning in PURE Rust

SDK for running DeepLabCut on a live video stream

Fast inference engine for Transformer models

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Born is a modern ML framework for Go — train and deploy models as single binaries. Pure Go, zero CGO, GPU accelerated.

Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

Crater is a cloud-native AI training & inference platform.

Neural Network primitives with multiple backends

Train Large Language Models on MLX.

Optimized primitives for collective multi-GPU communication

SKaiNET makes local AI practical for developers: simple to build with, multiplatform by design, and optimized for native performance without compromises.

Tools for handling multimodal data in machine learning projects.

Visualizer for neural network, deep learning and machine learning models

An open source implementation of CLIP.

Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/

JAX-based neural network library

A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.

A Data Streaming Library for Efficient Neural Network Training

Make drawing and labeling bounding boxes a piece of cake

Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything (SAM+SAM2/2.1+SAM3), MobileSAM!!

SemiBin: metagenomics binning with self-supervised deep learning

End-to-end ML toolkit automating the complete workflow — data profiling, preprocessing, feature engineering, model selection, training, validation, explainability, drift monitoring, fairness checks, and interactive HTML reporting. Includes a full CLI for zero-code ML pipelines. Python 3.10+.

Synthetic data curation for post-training and structured data extraction