July 2017
Intermediate to advanced
382 pages
9h 13m
English
Content preview from Machine Learning for OpenCV
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Summary
In this chapter, we learned about SVMs in all their forms and flavors. We now know how to draw decision boundaries in 2D and hyperplanes in high-dimensional spaces. We learned about different SVM kernels, and got to know how to implement them in OpenCV.
In addition, we also applied our newly gained knowledge to the practical example of pedestrian detection. For this, we had to learn about the HOG feature descriptor, and how to collect suitable data for the task. We used bootstrapping to improve the performance of our classifier and combined the classifier with OpenCV's multi-scale detection mechanism.
Not only was that a lot to digest in a single chapter, but you have also made it through half of the book. Congrats!
In the next chapter, ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.