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kubeflow - Machine Learning Toolkit for Kubernetes
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.
ml kubernetes minikube tensorflow notebook jupyterhub google-kubernetes-enginehandson-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
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.
tensorflow scikit-learn machine-learning deep-learning neural-network ml distributed jupyter-notebookmachinelearning - ML.NET is an open source and cross-platform machine learning framework for .NET.
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.
machine-learning algorithms mlcaffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.
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.
deep-learning machine-learning ai artificial-intelligence caffe2 deep-neural-networks mlpolyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes
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.
deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8salpaca - Functional programming inspired by ML for the Erlang VM
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.
ml erlang-vm alpaca statically-typed hindley-milnerTransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning
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.
ml automl transformations estimators dsl pipelines machine-learning salesforce einstein features feature-engineering spark sparkml ai automated-machine-learning transmogrification transmogrify structured-data transformersmachine-learning-with-ruby - Curated list: Resources for machine learning in 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 awesome-list list ml machine-learning ruby-gem rubyml rubynlpguess - Libraries & tools for enabling Machine Learning driven user-experiences on the web
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.
machine-learning performance web-performance prefetch prerender bundling webpack ml ai analytics recommendationkubeflow - Machine Learning Toolkit for Kubernetes
Please refer to the official docs at kubeflow.org. Please refer to the Community page.
ml kubernetes minikube tensorflow notebook jupyterhub google-kubernetes-engineAwesome-CoreML-Models - Largest list of models for Core ML (for iOS 11+)
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.
coreml coreml-model apple machine-learning curated-list coreml-framework coreml-models coremltools awesome-list models model download awesome core-ml ml caffe caffemodel tensorflow-models ios ios11ml - Machine learning tools in 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.
machine-learning ml machine learning data mining dataminingcaffe2 - Caffe2 is a lightweight, modular, and scalable deep learning framework.
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.
deep-learning machine-learning ai artificial-intelligence caffe2 deep-neural-networks mlnode-tensorflow - Node.js + TensorFlow
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.
tensorflow node-tensorflow nodejs machine-learning deep-learning npm-package tf tensor ml ai neural-networks neuralnetworks deeplearning model numerical-computation googleNiftyNet - An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
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.
tensorflow distributed ml neural-network python2 python3 pip deep-neural-networks deep-learning convolutional-neural-networks medical-imaging medical-image-computing medical-image-processing medical-images segmentation gan autoencoder medical-image-analysis image-guided-therapyMMLSpark - Microsoft Machine Learning for Apache Spark
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.
machine-learning spark cntk pyspark azure microsoft-machine-learning microsoft mlgocaml - :camel: Practical statically typed functional programming language implementation with Go and LLVM
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.
compiler ml programming-language llvm languagehub - A library for transfer learning by reusing parts of TensorFlow models.
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.
tensorflow machine-learning transfer-learning embeddings image-classification mlSparkLearning - Learning Apache spark,including code and data .Most part can run local.
Learning Apache spark,including code and data .Most part can run local.
spark learning sparkcore mlguess-next - 🔮 Demo application showing the integration of Guess.js with Next.js
Guess.js is a collection of libraries & tools for enabling data-driven user-experience on the web. In this particular example, we combine Guess.js with Next.js to introduce predictive prefetching of JavaScript bundles. Based on user navigation patterns collected from Google Analytics or other source, Guess.js builds a machine-learning model to predict and prefetch JavaScript that will be required in each subsequent page.
next-js guess-js predictive-prefetching ml web-performance
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