Displaying 1 to 20 from 88 results

review - Visual regression testing tool for responsive websites

  •    Javascript

Updating large and possibly responsively designed sites can be a hassle. You never know whether your change breakes anything on the other end of your sitemap, or in a certain resolution, except if have a look at every individual page...in every resolution you care about.The idea of review is not to test everything (and visual testing is hard) but rather use the human mind's excellent ability to quickly scan information and filter out what you need, i.e. what is broken.

natural - general natural language facilities for node

  •    Javascript

"Natural" is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.It's still in the early stages, so we're very interested in bug reports, contributions and the like.

TensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow

  •    Jupyter

This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.




keras-yolo2 - Easy training on custom dataset

  •    Jupyter

This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend. It supports training YOLOv2 network with various backends such as MobileNet and InceptionV3. Links to demo applications are shown below. Check out https://experiencor.github.io/yolo_demo/demo.html for a Raccoon Detector demo run entirely in brower with DeepLearn.js and MobileNet backend (it somehow breaks in Window). Source code of this demo is located at https://git.io/vF7vG.

mlr - mlr: Machine Learning in R

  •    R

Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

  •    Jupyter

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

mlpack - mlpack: a scalable C++ machine learning library --

  •    C++

mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings. Citations are beneficial for the growth and improvement of mlpack.


PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

  •    Python

In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

simple-statistics - simple statistics for node & browser javascript

  •    Javascript

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.

owl - Owl is an OCaml library for scientific and engineering computing.

  •    OCaml

Owl is an emerging numerical library for scientific computing and engineering. The library is developed in the OCaml language and inherits all its powerful features such as static type checking, powerful module system, and superior runtime efficiency. Owl allows you to write succinct type-safe numerical applications in functional language without sacrificing performance, significantly reduces the cost from prototype to production use. Owl's documentation contains a lot of learning materials to help you start. The full documentation consists of two parts: Tutorial Book and API Reference. Both are perfectly synchronised with the code in the repository by the automatic building system. You can access both parts with the following link.

regression-js - Curve Fitting in JavaScript.

  •    Javascript

regression-js is a JavaScript module containing a collection of linear least-squares fitting methods for simple data analysis. This module works on node and in the browser. It is available as the 'regression' package on npm. It is also available on a CDN.

Support Vector Machines Data Mining Plug-in in Analysis Services

  •    

The datamining Support Vector Machine (SVM) plug-in in MS SQL Server Analysis Services 2008. This plug-in is the SVM classification algorithm in addition to the shipped data mining algorithms with SQL Server.

ThunderSVM - A Fast SVM Library on GPUs and CPUs

  •    C++

The mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. It supports all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs. It can use same command line options as LibSVM. It supports Python, R and Matlab interfaces.

dana - Test/benchmark regression and comparison system with dashboard

  •    Javascript

See Principes documentation to understand key concepts of Dana.Dana provides some APIs to add builds, series and samples. APIs are accessible using POST http requests or using a node client using WebSockets. See APIs documentation for details.

grunt-phantomflow - Testing user flows

  •    Javascript

Unmaintained notice: As of December 22nd 2017 this project will no longer be maintained. What started out as a creative experiment four years ago became a significant tool in Huddle's Web development workflow, as well as the workflows of external Web engineers. But like PhantomCSS, its time to move on. If you are unfamilar with Grunt check out the Getting Started guide, as it explains how to create a Gruntfile as well as install and use Grunt plugins.

YCML - A Machine Learning and Optimization framework for Objective-C and Swift (MacOS and iOS)

  •    Objective-C

YCML is an Artificial Intelligence, Machine Learning and Optimization framework written in Objective-C. YCML can be used both in Objective-C as well as in Swift. YCML has been verified to run on MacOS and iOS. Above all, YCML attempts to bring high-quality published algorithms to Swift/Objective-C, using optimized implementations. Referenced papers for the implementation of each algorithm are available at the end of this document.