Download e-book for iPad: Mastering .NET Machine Learning by Jamie Dixon

By Jamie Dixon

Master the artwork of desktop studying with .NET and achieve perception into real-world applications

About This Book

  • Based on .NET framework 4.6.1, contains examples on ASP.NET middle 1.0
  • Set up your small business program to begin utilizing laptop studying techniques
  • Familiarize the consumer with a number of the extra universal .NET libraries for desktop learning
  • Implement a number of universal computing device studying techniques
  • Evaluate, optimize and regulate laptop studying models

Who This booklet Is For

This publication is concentrated at .Net builders who are looking to construct complicated desktop studying structures. a few uncomplicated realizing of knowledge technology is required.

What you are going to Learn

  • Write your personal laptop studying functions and experiments utilizing the newest .NET framework, together with .NET center 1.0
  • Set up your online business software to begin utilizing computer learning.
  • Accurately expect the longer term utilizing regressions.
  • Discover hidden styles utilizing choice trees.
  • Acquire, arrange, and mix datasets to force insights.
  • Optimize company throughput utilizing Bayes Classifier.
  • Discover (more) hidden styles utilizing KNN and Naive Bayes.
  • Discover (even extra) hidden styles utilizing K-Means and PCA.
  • Use Neural Networks to enhance enterprise choice making whereas utilizing the newest ASP.NET technologies.
  • Explore “Big Data”, disbursed computing, and the way to set up desktop studying types to IoT units – making machines self-learning and adapting
  • Along the best way, know about Open info, Bing maps, and MBrace

In Detail

.Net is among the conventional structures for constructing purposes. With the meteoric upward thrust of laptop studying, builders are actually partial to checking out how can they make their .Net functions smarter. additionally, .NET builders have an interest into entering into the realm of units and the way to use desktop studying thoughts to, good, machines.

This e-book is filled with real-world examples to simply use laptop studying innovations on your company purposes. you are going to commence with creation to F# and get ready yourselves for laptop studying utilizing .NET framework. you may be writing an easy linear regression version utilizing an instance which predicts revenues of a product. Forming a base with the regression version, you'll begin utilizing desktop studying libraries on hand in .NET framework resembling Math.NET, Numl.NET and Accord.NET with the aid of a pattern program. you are going to then movement directly to writing a number of linear regressions and logistic regressions.

You will examine what's open information and the awesomeness of style services. subsequent, you will handle a few of the matters that we have got been glossing over up to now and take a deep dive into acquiring, cleansing, and organizing our information. you'll evaluate the software of establishing a KNN and Naive Bayes version to accomplish very best results.

Implementation of Kmeans and PCA utilizing Accord.NET and Numl.NET libraries is roofed with the aid of an instance program. we are going to then examine lots of concerns confronting growing real-world computer studying versions like overfitting and the way to strive against them utilizing confusion matrixes, scaling, normalization, and have choice. you are going to now input into the area of Neural Networks and flow your line of industrial software to a hybrid clinical program. once you have coated the entire above laptop studying types, you will discover tips to take care of very huge datasets utilizing MBrace and the way to installation desktop studying versions to web of factor (IoT) units in order that the computer can research and adapt at the fly

Style and technique

This ebook will consultant you in studying every thing approximately tips to take on the flood of knowledge being encountered nowadays on your .NET purposes with the aid of well known computer studying libraries provided via the .NET framework.

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Mastering .NET Machine Learning by Jamie Dixon

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