Selection — 3 881

H2O and Spark interoperability.

Scala Library/REPL for Machine Learning Research.

Flexible Declarative Learning-Based Programming.

Simply written algorithms to help study ML or write your own implementations.

An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.

Strongly-typed Scala API for TensorFlow.

A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.

Neural network inference from the command line, implemented in CHICKEN Scheme.

Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.

Highly optimized artificial intelligence and machine learning library written in Swift.

a next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond.

The iOS and OS X neural network framework.

A bare bones library that includes a general matrix language and wraps some OpenCV for iOS development. [Deprecated]

A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians.

A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.

The first neural network / machine learning library written in Swift. This is a project for AI algorithms in Swift for iOS and OS X development. This project includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc...

Swift Language Bindings of TensorFlow. Using native TensorFlow models on both macOS / Linux.

A library for machine learning that builds predictions using a linear regression.

A curated list of pretrained CoreML models.

A curated list of machine learning models in CoreML format.

On-device streaming speech recognition SDK for iOS with Swift bindings (SPM). Based on NVIDIA NeMo FastConformer (80 ms cache-aware lookahead). Companion Silero VAD, wake-word, and 14-command KWS via same runtime.

A curated list of awesome Keras projects, libraries and resources.

A list of all things related to TensorFlow.

The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20

Build semantic search applications and workflows.

All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).

A starter kit for Jupyter notebooks and machine learning. Companion docker images consist of all combinations of python versions, machine learning frameworks (Keras, PyTorch and Tensorflow) and CPU/CUDA versions.

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.

Data Science Version Control is an open-source version control system for machine learning projects with pipelines support. It makes ML projects reproducible and shareable.

Python library for experiment metrics logging into simply formatted local files.

Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.

a lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.

Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.

A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.

A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.

ML powered analytics engine for outlier/anomaly detection and root cause analysis.