MLKit

ML framework

A framework for implementing machine learning algorithms in Swift to make it easier for developers to incorporate ML into their projects.

A simple machine learning framework written in Swift 🤖

GitHub

152 stars
13 watching
14 forks
Language: Swift
last commit: about 6 years ago
Linked from 1 awesome list

artificial-intelligencebackpropagationfeedforward-neural-networkgenetic-algorithmkmeanskmeans-clusteringlasso-regressionlinear-regressionmachine-learningmachine-learning-algorithmsmachine-learning-librarymlkitneural-networkpolynomial-regressionregressionridge-regressionswift

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
gianlucabertani/machinelearning A machine learning framework for native code on Macs with support for neural networks and natural language processing. 37
tensorflow/tensorflow An end-to-end platform for building and deploying machine learning applications 186,382
lehy/ocaml-sklearn Enables machine learning with scikit-learn in OCaml 34
mlr-org/mlr Provides an infrastructure for machine learning in R, enabling users to focus on experiments without writing lengthy wrappers and boilerplate code. 1,643
microsoft/0xdeca10b A framework for hosting and training machine learning models on a blockchain, enabling secure sharing and prediction without requiring users to pay for data or model updates. 556
mlr-org/mlr3 Provides an object-oriented framework for efficient machine learning in R 945
sethjuarez/numl Provides a set of reusable C# components for building machine learning models in .NET 430
mlreef/mlreef A platform for collaboration, data management, and reproducibility in machine learning development 1,442
samsung/veles A distributed machine learning platform for rapid Deep learning application development 906
a3data/hermione A framework for simplifying machine learning development and deployment 207
symjax/symjax A symbolic programming library for machine learning and deep learning applications. 120
jedld/tensor_stream A Ruby-based framework for building and deploying machine learning models on various hardware platforms. 507
h2oai/article-information-2019 A framework for building and evaluating machine learning systems with high accuracy and interpretability, particularly in human-centered applications. 13
giuse/machine_learning_workbench A comprehensive framework for practical machine learning in Ruby. 20
pmerienne/trident-ml A real-time online machine learning library built on top of Storm and Trident. 382