Titlebook: Machine Learning Projects for .NET Developers; Mathias Brandewinder Book 2015 Mathias Brandewinder 2015

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蒙太奇

978-1-4302-6767-6Mathias Brandewinder 2015

使人煩燥

Conclusion,r two along the way. Before we part ways, I figured it might be worthwhile to take a look back at what we have accomplished together, and perhaps also see if there are some broader themes that apply across the chapters, in spite of their profound differences.

superfluous

conquer

Sinus-Rhythm

煩憂

256 Shades of Gray,s it different from statistics? On the surface, machine learning might appear to be an exotic and intimidating specialty that uses fancy mathematics and algorithms, with little in common with the daily activities of a software engineer.

Provenance

Of Bikes and Men,eristics of a used car (age, miles, engine size, and so forth), how would you go about predicting how much it is going to sell for? This problem doesn‘t really fit the pattern of classification. What we need here is a model that differs from classification models in at least two aspects:

predict

Digits, Revisited, discovering machine learning concepts in the process. By contrast, this chapter is more intended as a series of practical tips which can be useful in various situations. We will use the digit recognizer model we created in . as a familiar reference point, and use it to illustrate techniques that are broadly applicable to other situations.