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Machine Theory

Mastering .NET Machine Learning by Jamie Dixon

By Jamie Dixon

About This Book

  • Based on .NET framework 4.6.1, comprises examples on ASP.NET center 1.0
  • Set up your small business software to begin utilizing desktop studying techniques
  • Familiarize the person with a few of the extra universal .NET libraries for computer learning
  • Implement numerous universal computer studying techniques
  • Evaluate, optimize and alter computing device studying models

Who This e-book Is For

This ebook is concentrated at .NET builders who are looking to construct advanced desktop studying structures. a few uncomplicated figuring out of knowledge technology is required.

What you'll Learn

  • Write your personal computer studying functions and experiments utilizing the newest .NET Framework, together with .NET center 1.0
  • Set up your corporation program to begin utilizing desktop learning
  • Accurately expect the way forward for your info utilizing uncomplicated, a number of, and logistic regressions
  • Discover hidden styles utilizing determination trees
  • Acquire, arrange, and mix datasets to force insights
  • Optimize company throughput utilizing Bayes Classifier
  • Discover (more) hidden styles utilizing k-NN and Naive Bayes
  • Discover (even extra) hidden styles utilizing k-means and PCA
  • Use Neural Networks to enhance company choice making whereas utilizing the most recent ASP.NET technologies

In Detail

.NET is likely one of the frequent systems for constructing purposes. With the meteoric upward thrust of computing device studying, builders are actually partial to checking out the way to make their .NET functions smarter utilizing computer learning.

Mastering .NET computing device studying is choked with real-world examples to give an explanation for tips to simply use computing device studying suggestions on your company purposes. you are going to start with an creation to F# and get ready yourselves for computing device studying utilizing the .NET Framework. you'll then how to write an easy linear regression version and, forming a base with the regression version, you'll begin utilizing computing device studying libraries to be had in .NET Framework corresponding to Math.NET, numl, and Accord.NET with examples. subsequent, you'll take a deep dive into acquiring, cleansing, and organizing your info. you are going to examine the implementation of k-means and PCA utilizing Accord.NET and numl libraries. you may be utilizing Neural Networks, AzureML, and Accord.NET to remodel your program right into a hybrid clinical software. additionally, you will see how one can care for very huge datasets utilizing MBrace and install computer studying types to IoT units in order that the laptop can study and adapt at the fly.

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Additional resources for Mastering .NET Machine Learning

Sample text

NET Framework. There is really no consensus in the F# community about which way is more idiomatic, though the style guidelines argue for the non-pipe forward way. Since both are supported by the languages and widely used, I use both depending on the code. Typically, when I have a string of thoughts to push together I use the pipe operator, but if there is only one calculation, I just call the function directly. Notice that I did this after syntax technique in all three lines: mean, deltas, and the return of the function.

Also, there is no return statement for the function, instead, the final expression of any function is always returned as the result. The last thing to note is that whitespace is important so that the indentation is required. fsx(8,1): warning FS0058: Possible incorrect indentation: this token is offside of context started at position (7:1). NET, it needs to use something to separate code. That separation is whitespace. NET background. If you have a background in R or Python, this should seem natural to you.

As you may expect, sumBy computes the sum of things. The key thing to realize is that the sumBy expects a function to be passed, not a data structure. 075. Laying them out on the same grid, you can see that the predicted numbers are close to the actual: These are still different enough on the plot that we eye-balled earlier with the red line going directly through 1, 2, 3, 4, 5 (solid line) and the regression line taking a slightly different path (dashed line): [ 32 ] Chapter 2 We will revisit how good this regression is at describing our data (and making predictions) in a bit.

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