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Published directory entries matching your search.
Machine Learning Mastery is a website that provides educational content such as tutorials, quick start guides, and eBooks to help developers learn and apply machine learning concepts and algorithms. It covers topics like deep learning, computer vision, natural language processing, and specific libraries including TensorFlow, PyTorch, and scikit-learn.
Magenta is a research project exploring machine learning in art and music creation, developing deep learning and reinforcement learning algorithms for generating songs, images, and drawings. It also explores tools letting artists and musicians extend their processes with these models. The repository is archived and read-only.
A repository of material for the exercise classes of a Big Data Analytics master course. Hands-on sessions cover Spark and its machine learning library, Map/Reduce implementations of linear regression and alternating least squares, streaming analytics with Recursive Least Squares, and image classification with convolutional neural networks. Material is provided as Jupyter notebooks.
MatlabBGL is a Matlab package for working with graphs that uses the Boost Graph Library to implement graph algorithms. It is designed for large sparse graphs with hundreds of thousands of nodes and was written by David Gleich. The page lists version 4.0 as the latest release, with downloads hosted on the Matlab File Exchange.
Metric Learning Algorithms in Python.
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.
Algorithm capable of fully capturing the impact of data drift on performance.
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.
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.
OPFython is a GitHub repository offering a Python-inspired implementation of the Optimum-Path Forest classifier. The library enables classification tasks using the optimum-path forest algorithm. It includes installation instructions, quick start guides, and classifier documentation. The project is open source and welcomes contributions from the community.
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.
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.
