Dixon J. - Mastering .NET Machine Learning [2016, PDF, ENG] torrent

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Mastering .NET Machine Learning

Год издания: 2016
Автор: Dixon J.

Издательство: Packt Publishing
ISBN: 9781785888403
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 359

Описание: .Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines.

This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions.

You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results.

Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly.

What You Will Learn

- Write your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0
- Set up your business application to start using machine learning.
- Accurately predict the future using regressions.
- Discover hidden patterns using decision trees.
- Acquire, prepare, and combine datasets to drive insights.
- Optimize business throughput using Bayes Classifier.
- Discover (more) hidden patterns using KNN and Naïve Bayes.
- Discover (even more) hidden patterns using K-Means and PCA.
Use Neural Networks to improve business decision making while using the latest ASP.NET technologies.
Explore “Big Data”, distributed computing, and how to deploy machine learning models to IoT devices – making machines self-learning and adapting
Along the way, learn about Open Data, Bing maps, and MBrace
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Table of Contents

1: Welcome to Machine Learning Using the .NET Framework
2: AdventureWorks Regression
3: More AdventureWorks Regression
4: Traffic Stops – Barking Up the Wrong Tree?
5: Time Out – Obtaining Data
6: AdventureWorks Redux – k-NN and Naïve Bayes Classifiers
7: Traffic Stops and Crash Locations – When Two Datasets Are Better Than One
8: Feature Selection and Optimization
9: AdventureWorks Production – Neural Networks
10: Big Data and IoT
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