By Jamie Dixon
About This Book
- Based on .NET framework 4.6.1, comprises examples on ASP.NET center 1.0
- Set up your enterprise software to begin utilizing computing device studying techniques
- Familiarize the consumer with a number of the extra universal .NET libraries for computing device learning
- Implement a number of universal computer studying techniques
- Evaluate, optimize and regulate desktop studying models
Who This e-book Is For
This e-book is focused at .NET builders who are looking to construct advanced desktop studying platforms. a few uncomplicated knowing of information technology is required.
What you are going to Learn
- Write your individual computing device studying purposes and experiments utilizing the most recent .NET Framework, together with .NET center 1.0
- Set up your corporation software to begin utilizing laptop learning
- Accurately expect the way forward for your info utilizing uncomplicated, a number of, and logistic regressions
- Discover hidden styles utilizing determination trees
- Acquire, organize, 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 selection making whereas utilizing the most recent ASP.NET technologies
In Detail
.NET is without doubt one of the common systems for constructing functions. With the meteoric upward thrust of desktop studying, builders at the moment are a fan of checking out how one can make their .NET functions smarter utilizing laptop learning.
Mastering .NET desktop studying is jam-packed with real-world examples to give an explanation for the way to simply use computing device studying suggestions on your company purposes. you'll commence with an advent to F# and get ready yourselves for computer 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 are going to begin utilizing desktop studying libraries on hand in .NET Framework corresponding to Math.NET, numl, and Accord.NET with examples. subsequent, you will take a deep dive into acquiring, cleansing, and organizing your info. you are going to study the implementation of k-means and PCA utilizing Accord.NET and numl libraries. you'll be utilizing Neural Networks, AzureML, and Accord.NET to remodel your software right into a hybrid medical software. additionally, you will see easy methods to care for very huge datasets utilizing MBrace and set up computing device studying versions to IoT units in order that the computing device can research and adapt at the fly.
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Sample text
If we are then given a number that is on our graph, we can make a prediction about where it will land. For example, if we are given another 2, we can predict that we will probably result in a 2. Not only that, we can make predictions about where the line is headed (slope) for inputs that we have not seen before. For example, if we input 6, we can guess that it will probably be close to 6. In a real-word example, we typically don't have a single input for a given number. 75. 75. With this mental model in place, let's go ahead and create a simple linear regression from scratch.
8. Sum up y2. 9. Do the final formula. sumBy (fun (xy, / sqrt (sx2*sy2) -> x*y, x2, y2) x2, y2) x2, y2) x*x, y*y) -> xy) -> x2) -> y2) Sending that to the REPL gives us: val pearsonsCorrelation : a:seq
Here, input is an array of tuples. A tuple is a data structure that contains groups of data that are unnamed—usually there are two items. The types do not have to be the same as a tuple's items. If you are familiar with the concept of a key/value pair, you can use that as a mental model of a tuple. The only real "gotcha" is that tuples can have many items so this it is a perfectly valid tuple: 2,true,"dog" where the first position is an int, the second is a Boolean, and the third is a string.