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Published directory entries matching your search.
ml.js is an open-source machine learning toolset implemented in JavaScript, hosted in the mljs/ml GitHub repository. It provides libraries for both supervised and unsupervised machine learning tasks, alongside usage resources and standalone example code for users.
ml5.js is a JavaScript library providing access to machine learning algorithms and models in the browser, building on TensorFlow.js. It aims to make machine learning approachable for artists, creative coders and students, and is supported by code examples, tutorials and sample data sets. The project is described as currently in development.
NNI is an open source AutoML toolkit developed by Microsoft. It automates tasks across the machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning for machine learning workflows.
Open-source, low-code machine learning library in Python that automates machine learning workflows.
Swift for TensorFlow is an archived GitHub repository describing an experiment in a next-generation machine learning platform that combined machine learning, compilers and differentiable programming. It was archived in February 2021, and the repository is read-only; the site states it will not receive further updates.
TPOT (Tree-based Pipeline Optimization Tool) is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming. The project underwent a major refactoring, merging what was once called TPOT2 into the main package, with new features including genetic feature selection, multi-objective optimization and an expanded method of defining search spaces.
CML, or Continuous Machine Learning, is an open-source CLI tool for implementing CI/CD with a focus on MLOps. It is used to automate development workflows, including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. It can generate a visual report with results and metrics automatically on every pull request, using GitLab, GitHub, or Bitbucket.
InterpretML is an open-source package that collects machine learning interpretability techniques. It allows training interpretable glassbox models and explaining blackbox systems, to understand a model's global behavior or the reasons behind individual predictions. It includes the Explainable Boosting Machine, developed at Microsoft Research, plus other glassbox models and blackbox explainers.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
neonrvm is an open-source machine learning library for performing regression tasks using the Relevance Vector Machine (RVM) technique. It is written in Python and available on GitHub. The library aims to provide efficient implementations for RVM-based regression.
Awesome AutoML Papers is a GitHub-hosted curated list focused on automated machine learning (AutoML). It aggregates academic papers, articles, tutorials, slide decks, and related projects in the AutoML field, serving as a centralized reference for users exploring automated machine learning research and developments.
AutoKeras is an AutoML library for deep learning built on Keras, developed by DATA Lab at Texas A&M University. The project describes its goal as making machine learning accessible to everyone. It can be installed with pip, and the documentation states it is currently only compatible with Python 3.7 or later and TensorFlow 2.8.0 or later.
Cogitare is a modern, fast, modular deep learning and machine learning framework for Python built on top of PyTorch, with a user-friendly interface for beginners and advanced toolsets for experts.
Deeplearning4j is an open-source suite of tools for deploying and training deep learning models using the Java Virtual Machine (JVM). It supports model import from Keras, TensorFlow, and ONNX/PyTorch, includes a modular C++ math library with a corresponding Java library built on top, and offers SameDiff, a library for deep learning operations similar to PyTorch and TensorFlow.
Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform.
FedML is a unified and scalable open-source machine learning library for large-scale distributed training, model serving, and federated learning. It includes FEDML Launch, a cross-cloud scheduler for running AI workloads on GPU clouds or on-premise clusters, and forms the foundation of the TensorOpera AI generative AI platform.
fenn is a Python framework for building machine learning and deep learning workflows and LLM agents. It provides prebuilt trainers, agent templates, logging, and configuration management, with YAML configuration parsed and injected into the entrypoint with CLI override support. It is released under the MIT license.
General Machine Learning library using Numenta’s Cortical Learning Algorithm.
JSMLT is an open source JavaScript library for education in machine learning. It implements supervised learning algorithms, including SVM, random forest, decision tree, perceptron, neural networks, k-nearest neighbors and logistic regression, and provides visualization examples for exploring how the algorithms work.
ML-From-Scratch is a GitHub repository that provides bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility, aiming to cover methods from linear regression to deep learning.
MLKit is a simple machine learning framework written in Swift. Currently it features algorithms dealing with regression, with planned expansion to classification, clustering, recommender systems and deep learning. The project is under active development and is not ready for use in commercial or personal projects, and can be installed via CocoaPods as MachineLearningKit.
Netron is a viewer for neural network, deep learning and machine learning models. It supports formats including ONNX, TensorFlow Lite, PyTorch, TensorFlow, Core ML, OpenVINO, Keras, Caffe, Darknet, Safetensors and NumPy. It is available as a browser version and installers for macOS, Linux and Windows, plus a Python package. Experimental support exists for MLIR, JAX, GGUF and other formats.
ONNX-Scala is an ONNX (Open Neural Network eXchange) API and backend built for typeful, functional deep learning and classical machine learning use cases in Scala 3. It is hosted as a public repository under the EmergentOrder GitHub organization.
OpenMetricLearning is a repository that provides metric learning and retrieval pipelines, pre-trained models, and a model zoo for related machine learning tasks. It includes documentation, installation guides, and example implementations for users.
