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Advanced machine learning with R

Author: Tim Hoolihan
Publisher: [Place of publication not identified] : Packt, [2017]
Edition/Format:   eVideo : Clipart/images/graphics : English
Summary:
"In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them. After that, you’ll see how to tune  Read more...
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Details

Material Type: Clipart/images/graphics, Internet resource, Videorecording
Document Type: Internet Resource, Computer File, Visual material
All Authors / Contributors: Tim Hoolihan
OCLC Number: 1004966118
Notes: Title from title screen (viewed September 26, 2017).
Date of publication from resource description page.
Performer(s): Presenter, Tim Hoolihan.
Description: 1 online resource (1 streaming video file (1 hr., 32 min.)) : digital, sound, color
Responsibility: Tim Hoolihan.

Abstract:

"In this course, you’ll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, you’ll learn all about neural networks through an example of DNA classification data. You’ll explore networks, implement them, and classify them. After that, you’ll see how to tune hyper-parameters using a data set of sonar data and you’ll get to know their properties. Next, you’ll understand unsupervised learning with an example of clustering politicians, where you’ll explore new patterns, understand unsupervised learning, and visualize and cluster the data. Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, we’ll offer some suggestions for those who wish to practice the concepts further."--Resource description page.

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Linked Data


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