Displaying 1 to 20 from 67 results

kubeflow - Machine Learning Toolkit for Kubernetes

  •    Python

The Kubeflow project is dedicated to making machine learning on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to train, test, and deploy best-of-breed open-source predictive models to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run KubeFlow.This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs.

handson-ml - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow

  •    Jupyter

First, you will need to install git, if you don't have it already. If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.

machinelearning - ML.NET is an open source and cross-platform machine learning framework for .NET.

  •    CSharp

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers. ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

caffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.

  •    C++

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.




polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

  •    Python

Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

alpaca - Functional programming inspired by ML for the Erlang VM

  •    Erlang

Alpaca is a statically typed, strict/eagerly evaluated, functional programming language for the Erlang virtual machine (BEAM). At present it relies on type inference but does provide a way to add type specifications to top-level function and value bindings. It was formerly known as ML-flavoured Erlang (MLFE). Please see the rebar3 plugin documentation for more details.

TransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning

  •    Scala

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time. Skip to Quick Start and Documentation.

machine-learning-with-ruby - Curated list: Resources for machine learning in Ruby.

  •    Ruby

Machine Learning is a field of Computational Science - often nested under AI research - with many practical applications due to the ability of resulting algorithms to systematically implement a specific solution without explicit programmer's instructions. Obviously many algorithms need a definition of features to look at or a biggish training set of data to derive the solution from. This curated list comprises awesome libraries, data sources, tutorials and presentations about Machine Learning utilizing the Ruby programming language.


Awesome-CoreML-Models - Largest list of models for Core ML (for iOS 11+)

  •    HTML

We've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques. We've created a site with better visualization of the models CoreML.Store, and are working on more advance features. If you've converted a Core ML model, feel free to submit an issue.

Skater - Python Library for Model Interpretation/Explanations

  •    Python

Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The project was started as a research idea to find ways to enable better interpretability(preferably human interpretability) to predictive "black boxes" both for researchers and practioners. The project is still in beta phase.

caffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.

  •    Shell

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.

guess - Libraries & tools for enabling Machine Learning driven user-experiences on the web

  •    TypeScript

Libraries and tools for enabling data-driven user-experiences on the web. Install and configure GuessPlugin - the Guess.js webpack plugin which automates as much of the setup process for you as possible.

kubeflow - Machine Learning Toolkit for Kubernetes

  •    Python

Please refer to the official docs at kubeflow.org. Please refer to the Community page.

ml - Machine learning tools in JavaScript

  •    Javascript

This library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often. We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find. It will be available as the global ML variable. The package is in UMD format and can be "required" within webpack or requireJS.

node-tensorflow - Node.js + TensorFlow

  •    Javascript

TensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.

NiftyNet - An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

  •    Python

NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.

MMLSpark - Microsoft Machine Learning for Apache Spark

  •    Scala

MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

gocaml - :camel: Practical statically typed functional programming language implementation with Go and LLVM

  •    Go

GoCaml is subset of OCaml in Go based on MinCaml using LLVM. GoCaml adds many features to original MinCaml. MinCaml is a minimal subset of OCaml for educational purpose. It is statically-typed and compiled into a binary. This project aims incremental compiler development for my own programming language. Type inference, closure transform, mid-level IR are implemented.

hub - A library for transfer learning by reusing parts of TensorFlow models.

  •    Python

TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. In particular, it provides modules, which are pre-trained pieces of TensorFlow models that can be reused on new tasks. If you'd like to contribute to TensorFlow Hub, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

SparkLearning - Learning Apache spark,including code and data .Most part can run local.

  •    Scala

Learning Apache spark,including code and data .Most part can run local.





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