February 2017
Intermediate to advanced
370 pages
8h 11m
English
Content preview from TensorFlow Machine Learning CookbookBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 4. Support Vector Machines
This chapter will cover some important recipes regarding how to use, implement, and evaluate support vector machines (SVM) in TensorFlow. The following areas will be covered:
- Working with a Linear SVM
- Reduction to Linear Regression
- Working with Kernels in TensorFlow
- Implementing a Non-Linear SVM
- Implementing a Multi-Class SVM
Note
Note that both the prior covered logistic regression and most of the SVMs in this chapter are binary predictors. While logistic regression tries to find any separating line that maximizes the distance (probabilistically), SVMs also try to minimize the error while maximizing the margin between classes. In general, if the problem has a large number of features compared to training examples, ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.