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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 rubynlpAwesome-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 ios11Skater - Python Library for Model Interpretation/Explanations
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.
ml predictive-modeling machine-learning modeling-tools model-interpretation blackbox datascience model-explanation explanation-system deep-learning deep-neural-networks attribution lstm-neural-networks cnn-classificationcaffe2 - 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 mlguess - 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 recommendationml - 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 dataminingnsfwjs - NSFW detection on the client-side via TensorFlow.js
A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. NSFWJS isn't perfect, but it's pretty accurate (~90% from our test set of 15,000 test images)... and it's getting more accurate all the time. Why would this be useful? Check out the announcement blog post.
machine-learning machinelearning tensorflowjs tensorflow-js node-module content-management nsfw-recognition nsfw ml machine learning tensorflow jskubeflow - Machine Learning Toolkit for Kubernetes
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.
ml kubernetes minikube tensorflow notebook jupyterhub google-kubernetes-engine machine-learningmetaflow - Build and manage real-life data science projects with ease.
Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. For more information, see Metaflow's website and documentation.
productivity data-science machine-learning r ai reproducible-research ml rstats r-package model-management ml-infrastructure mlops ml-platformattention-ocr - A Tensorflow model for text recognition (CNN + seq2seq with visual attention) available as a Python package and compatible with Google Cloud ML Engine
Visual attention-based OCR model for image recognition with additional tools for creating TFRecords datasets and exporting the trained model with weights as a SavedModel or a frozen graph. This project is based on a model by Qi Guo and Yuntian Deng. You can find the original model in the da03/Attention-OCR repository.
machine-learning ocr tensorflow google-cloud ml cnn seq2seq image-recognition hacktoberfest ocr-recognition google-cloud-mlmlflow - Open source platform for the machine learning lifecycle
MLflow requires conda to be on the PATH for the projects feature. Nightly snapshots of MLflow master are also available here.
machine-learning ai apache-spark ml model-management mlflowHub - Fastest dataset optimization and management for machine and deep learning
Note: the translations of this document may not be up-to-date. For the latest version, please check the README in English. Software 2.0 needs Data 2.0, and Hub delivers it. Most of the time Data Scientists/ML researchers work on data management and preprocessing instead of training models. With Hub, we are fixing this. We store your (even petabyte-scale) datasets as single numpy-like array on the cloud, so you can seamlessly access and work with it from any machine. Hub makes any data type (images, text files, audio, or video) stored in cloud usable as fast as if it were stored on premise. With same dataset view, your team can always be in sync.
training data-science machine-learning cloud ai computer-vision deep-learning tensorflow cv ml collaboration pytorch cloud-computing datasets dataset-generation data-processing data-version-control data-pipelines mlopsnode-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 google
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