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Published directory entries matching your search.
The Million Song Dataset is a freely available collection of audio features and metadata for one million contemporary popular music tracks, provided by The Echo Nest. It aims to support research on algorithms at commercial scale and provide a reference dataset for evaluation. It contains derived features, not audio, though sample audio can be fetched via provided code.
Mimi Panda is a service for converting images into coloring pages and paint by numbers, using graphic techniques and algorithms developed by its team. It offers free coloring pages and a paint by numbers converter, an API service for enterprise clients, and a subscription-based Mimi Profile service with higher quality and a personal account.
Machine Learning Visualized is a book of Jupyter Notebooks that implement and mathematically derive machine learning algorithms from first principles. Each notebook's output is a visualization of the algorithm during training, converging at its optimal weights. Chapters cover optimization, clustering and reduction, linear models and activation functions, and neural networks.
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.
ML.NET is an open source, cross-platform machine learning framework for .NET. It lets developers build, train, deploy and consume custom models in .NET applications, with data loading from files and databases, data transformations and many ML algorithms. Models can be trained for scenarios such as classification, forecasting and anomaly detection, and TensorFlow and ONNX models can be consumed within the framework.
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.
MLBase (MLBase.jl) is a Julia package hosted in the JuliaStats/MLBase.jl GitHub repository, providing a collection of functions to support the development of machine learning algorithms. It includes accompanying resources and documentation for package users.
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.
mlpack is a header-only C++ machine learning library with bindings to Python, Julia, R, Go, and the command line. It is built on Armadillo, ensmallen, and cereal and provides implementations of machine learning algorithms. The documentation lists classification, regression, clustering, and preprocessing facilities. It is open source under the 3-clause BSD license.
Framework that implements AutoML algorithms for model architecture search at scale.
A research-oriented computer science curriculum centered on computational trinitarianism, where computation appears as logic, programs and categories. It covers programming with functional languages, discrete math formalized in Lean 4, algorithms and proof assistants.
NeuPy is a Python library built on Tensorflow for prototyping and building neural networks. The repository notes that the library is no longer actively maintained. It includes examples and notebooks covering algorithms such as Boltzmann machines and self-organizing maps.
An NLP toolbox for Matlab under development by researchers at UC Berkeley's AMPLab, intended to process natural language within Matlab rather than switching to other languages. It includes modules for text tokenization, text preprocessing such as stop word removal and stemming, and learning algorithms including linear regression, decision trees, support vector machines and a Naive Bayes classifier.
Node-SVM is a GitHub repository that provides support vector machine (SVM) implementations for Node.js. The project enables developers to use SVM algorithms within JavaScript applications. It includes installation instructions, a quick start guide, and an API reference. The repository is open source and contributed to by the community.
Nosh is a product of Nosh Technologies, a deep tech company that builds algorithms, programs and mobile applications to optimize food management and reduce household food waste. Its flagship product is an AI-powered smartphone app that tracks expiry and use-by dates, offers an integrated shopping list, suggests recipes from available stock, and includes a shop feature for buying discounted food from local sellers.
numpy-ML is a GitHub-hosted library that implements various machine learning algorithms using only NumPy. The project emphasizes rapid experimentation and can be used as a package. It covers a range of ML methods and includes documentation for users. The repository is open source and welcomes contributions.
Oml is an OCaml math library that collects OCaml math and statistics functions. It aims for a descriptive, simple and typed approach to algorithms. A superset, Oml_full, includes C and Fortran dependencies such as Lacaml and LBFGS.
OpenCV is an open source computer vision library that contains over 2500 algorithms and supports model execution for machine learning and artificial intelligence. It was designed to be cross-platform, written in C with C++ and wrappers for Python and Java, and runs on desktop and mobile systems. A GPU module implemented with CUDA provides acceleration for part of the library.
OpenPuff is a portable steganography and watermarking tool for hiding data in carrier files. It supports image, audio, video, Flash and Adobe formats, and uses multiple passwords, 16 cryptography algorithms and carrier chains. It requires no installation and runs in user mode.
Optuna is an open source automatic hyperparameter optimization software framework designed for machine learning. It provides an imperative, define-by-run style user API with eager search spaces, algorithms that prune unpromising trials, and parallelization of searches over multiple threads or processes without modifying code. It is framework agnostic and can be installed with pip on Python 3.10 or newer.
ParaMonte is a serial and parallel library of Monte Carlo routines for sampling mathematical density functions of arbitrary dimensions and machine learning algorithms for scientific inference. It is designed for interoperability with C, C++, Fortran, MATLAB and Python, and supports shared-memory OpenMP threads and MPI distributed communications.
Park School Mathematics is a site presenting a series of mathematics books, from Book 1: Reasoning and Proving through Book 10: Functions and Models II, plus additional custom books. Book pages list lessons covering topics such as combinatorics, linear equations, quadratic models, graph theory, geometric proof, circular functions, algorithms, logarithms and recursion.
Parris, the automated infrastructure setup tool for machine learning algorithms.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks. Materials are organized into sections covering preprocessing, model evaluation, parameter estimation, algorithms, clustering and data visualization.
