Ml
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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 12-week, 26-lesson curriculum teaching classic machine learning using Scikit-learn through hands-on projects and quizzes.
A visualizer for neural network, deep learning, and machine learning models across multiple frameworks.
An open-source AI engineering platform for debugging, evaluating, monitoring, and optimizing production LLM applications and machine learning models.
Jupyter notebooks with example code and exercises from the first edition of Hands-on Machine Learning with Scikit-Learn and TensorFlow.
An open standard format for representing machine learning models to enable interoperability between frameworks.
An open-source AI memory tool that records your screen and audio locally, enabling search and automation agents based on your computer activity.
An open-source AI memory tool that captures your screen and audio locally, enabling search and automation agents based on your computer activity.
A composable, modular, and scalable machine learning toolkit for building AI platforms on Kubernetes.
A blazing-fast, lightweight deep learning inference engine from Alibaba, optimized for on-device LLMs and Edge AI.
An open-source LLMOps platform unifying gateway, observability, evaluation, optimization, and experimentation for industrial-grade LLM applications.
A research framework for fast prototyping of reinforcement learning algorithms, designed for easy experimentation and reproducibility.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
A lightweight, modular, and scalable deep learning framework built on the original Caffe.
An open-source feature store for managing and serving machine learning features for training and online inference.
An open-source, self-hosted ML experiment tracker with a performant UI and SDK for comparing and querying training runs.
A fast, flexible C++ standalone library for machine learning with high-performance defaults and total internal modifiability.
A fast, flexible C++ standalone library for machine learning with high-performance defaults and total internal modifiability.
An end-to-end framework for building custom AI applications and agents directly integrated with databases.
An open-source library for building massively scalable machine learning pipelines on Apache Spark.
An engine-agnostic deep learning framework for Java developers, providing a high-level API for model training and inference.
A collection of samples demonstrating how to use ML.NET for various machine learning tasks in .NET applications.
An open-source solution for continuous validation of machine learning models and data, from research to production.
A comprehensive collection of machine learning algorithms and mathematical utilities implemented in JavaScript for browser and Node.js.
A Scala API for Apache Beam and Google Cloud Dataflow, enabling unified batch and streaming data processing.
A curated list of libraries, tutorials, and resources for implementing machine learning in the Ruby programming language.
A curated list of awesome libraries, data sources, tutorials, and resources for machine learning using the Ruby programming language.
A Ruby gem for building LLM-powered applications with a unified interface for multiple providers, RAG systems, and AI assistants.
A Ruby library for building LLM-powered applications with a unified interface for multiple providers, RAG systems, and AI assistants.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
A comprehensive Python library for generating and analyzing multi-class confusion matrices with extensive statistical metrics.
A Python toolbox for explainable AI, providing tools for data analysis, model evaluation, and bias mitigation in machine learning.
A Ruby machine learning library with a Scikit-Learn-like interface for classification, regression, clustering, and dimensionality reduction.
A hands-on tutorial for training and deploying a machine learning model as a serverless REST API to predict cryptocurrency prices.
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