Showing 15 open source projects for "windows command code"

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  • 1
    emgucv

    emgucv

    Cross platform .Net wrapper to the OpenCV image processing library

    Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio and Unity, it can run on Windows, Linux, Mac OS, iOS and Android.
    Downloads: 11 This Week
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  • 2
    .NET for Apache Spark

    .NET for Apache Spark

    A free, open-source, and cross-platform big data analytics framework

    ...With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks.
    Downloads: 1 This Week
    Last Update:
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  • 3
    ML.NET

    ML.NET

    Open source and cross-platform machine learning framework for .NET

    With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that...
    Downloads: 1 This Week
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  • 4
    Unity ML-Agents Toolkit

    Unity ML-Agents Toolkit

    Unity machine learning agents toolkit

    Train and embed intelligent agents by leveraging state-of-the-art deep learning technology. Creating responsive and intelligent virtual players and non-playable game characters is hard. Especially when the game is complex. To create intelligent behaviors, developers have had to resort to writing tons of code or using highly specialized tools. With Unity Machine Learning Agents (ML-Agents), you are no longer “coding” emergent behaviors, but rather teaching intelligent agents to “learn”...
    Downloads: 1 This Week
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  • 5
    TensorFlow.NET

    TensorFlow.NET

    .NET Standard bindings for Google's TensorFlow for developing models

    TensorFlow.NET (TF.NET) provides a .NET Standard binding for TensorFlow. It aims to implement the complete Tensorflow API in C# which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. TensorFlow.NET has built-in Keras high-level interface and is released as an independent package TensorFlow.Keras. SciSharp STACK's mission is to bring popular data science technology into the .NET world and to provide .NET developers...
    Downloads: 0 This Week
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  • 6
    FlubuCore

    FlubuCore

    A cross platform build and deployment automation system

    "FlubuCore - Fluent Builder Core" is a cross-platform build and deployment automation system. You can define your build and deployment scripts in C# using an intuitive fluent interface. This gives you code completion, IntelliSense, debugging, FlubuCore custom analyzers, and native access to the whole .NET ecosystem inside of your scripts. FlubuCore offers a .net (core) console application that uses power of roslyn to compile and execute scripts. Intuitive and easy to learn. C#, fluent...
    Downloads: 0 This Week
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  • 7
    ML.NET Samples

    ML.NET Samples

    Samples for ML.NET, an open source and cross-platform machine learning

    ...In this GitHub repo, we provide samples that will help you get started with ML.NET and how to infuse ML into existing and new .NET apps. We're working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. The "best" model and the code for running it will be generated for you. The ML.NET CLI (command-line interface) is a tool you can run on any command prompt (Windows, Mac or Linux) for generating good quality ML.NET models based on training datasets you provide. ...
    Downloads: 1 This Week
    Last Update:
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  • 8
    Accord.NET Framework

    Accord.NET Framework

    Scientific computing, machine learning and computer vision for .NET

    The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono.
    Downloads: 5 This Week
    Last Update:
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  • 9

    Microsoft-Azures-Basic-C--Pull

    This is a simple C# Program that uses Microsoft Azures pull

    This is a simple C# Program that uses Microsoft Azures. In the image you will find an example of a program I created using the script. It's very quick and easy to setup. I provide some screenshots and tips on where to place what where. After you have placed in your Microsoft Azures API Key and Postman Client and Body. You now be able to insert inputs via code like the example in my image(s).
    Downloads: 0 This Week
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  • 10
    Multiple Back-Propagation (with CUDA)

    Multiple Back-Propagation (with CUDA)

    Open source software for training neural networks

    Multiple Back-Propagation is an open source software application for training neural networks with the backpropagation and the multiple back propagation algorithms. Currently this project is also hosted at http://code.google.com/p/multiplebackpropagation
    Downloads: 2 This Week
    Last Update:
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  • 11
    neural network designer

    neural network designer

    a dbms for neural nets. Chatbots, DTrees, random forests, n-grams,...

    This project consists out of a windows based designer application and a library (that can run on multiple platforms, including android) together with several demo applications (including an MVC3 chatbot client and an android application). It is probably best compared to a database management system, but for neural networks instead of relational data. As such, the library is optimized for handling any type of data-size by using advanced streaming and caching algorithms. With the designer,...
    Downloads: 0 This Week
    Last Update:
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  • 12
    HoldemAI

    HoldemAI

    Texas Holdem Poker AI

    Full ring Texas Hold'em poker game built around an intelligent AI system. The AI uses players' betting actions to calculate a probability distribution of their hole cards and uses it to evaluate hand strength and the best possible action. Small random changes are made to mimic human behavior and make the AI less predictable. Future versions will include adaptive opponent modeling using neural networks to improve the AI's strength. The AI code can be easily adapted for input from screen scrapers.
    Downloads: 0 This Week
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  • 13
    CRFSharp

    CRFSharp

    CRFSharp is a .NET(C#) implementation of Conditional Random Field

    CRFSharp(aka CRF#) is a .NET(C#) implementation of Conditional Random Fields, an machine learning algorithm for learning from labeled sequences of examples. It is widely used in Natural Language Process (NLP) tasks, for example: word breaker, postagging, named entity recognized, query chunking and so on. CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally...
    Downloads: 0 This Week
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  • 14
    SVM# is a svm(support vector machine) classification implemented in C#. The project contains both train and predict modules.
    Downloads: 0 This Week
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  • 15
    Medical Datasets (In a text file, with space separated values) can be loaded to the system. By choosing either one of the two classifiers, Neural network or Decision Tree, the system can be trained and evaluated.
    Downloads: 0 This Week
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