GitHub-проекты

Large scale Gaussian Mixture Models.

Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.

Flexible Deep Learning Framework in Julia.

A Julia package for manifold learning and nonlinear dimensionality reduction.

eXtreme Gradient Boosting Package in Julia.

Deep Learning framework for Julia inspired by Caffe. [Deprecated]

Julia artificial neural networks. [Deprecated]

A Julia package for non-negative matrix factorization.

Methods for dimensionality reduction.

Kernel density estimators for Julia.

SVM for Julia. [Deprecated]

Basic functions for clustering data: k-means, dp-means, etc.

Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.

[Deprecated]

Julia package for Gaussian processes.

Generalized linear models in Julia.

Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.

MCMC tools for Julia. [Deprecated]

A neural network in Julia.

Decision Tree Classifier and Regressor.

Julia module for Distance evaluation.

basic MCMC sampler implemented in Julia. [Deprecated]

A Julia package for fitting (statistical) mixed-effects models.

Simple Naive Bayes implementation in Julia. [Deprecated]

Local regression, so smooooth!

Algorithms for regression analysis (e.g. linear regression and logistic regression). [Deprecated]

Julia package for Regularized Discriminant Analysis.

A Julia framework for probabilistic graphical models.

A set of functions to support the development of machine learning algorithms.

Julia Machine Learning library. [Deprecated]

Beer glass classifier created with Synaptic.

Example of how the neural network learns to predict the angle between two points created with Synaptic.

A JavaScript application framework for machine learning and its engineering.

MLPleaseHelp is a simple ML resource search engine. You can use this search engine right now at https://jgreenemi.github.io/MLPleaseHelp/, provided via GitHub Pages.

Library for calculating great circle distance.

Linear Regression library. [Deprecated]

A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.

A JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work in all modern browsers (including IE) as well as in Node.js.

Vector and Matrix math for JavaScript. [Deprecated]

A standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

A JavaScript Native PyTorch-aligned Machine Learning Framework, built from scratch on WebGPU.

Fast Deep Neural Network JavaScript Framework. WebDNN uses next generation JavaScript API, WebGPU for GPU execution, and WebAssembly for CPU execution.

A deep learning library for the browser, accelerated by WebGL and WebAssembly.

Run XGBoost model and make predictions in Node.js.

Machine learning toolkit with classification and clustering for Node.js; supports visualization (see visualml.io).

Reinforcement learning using Markov Decision Processes.

Friendly machine learning for the web!

Machine learning and numerical analysis tools for Node.js and the Browser!