February 2018
Beginner to intermediate
258 pages
5h 47m
English
Content preview from R Deep Learning Projects
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Outlier detection in MNIST
All right, so admittedly our previous application has nothing to do with fraud or outlier detection so far. We can do a small modification on the previous setup to show how a similar framework works. For this, let's assume that the number 7 is an outlier class and we will try to identify it from the result of our normal numbers: 0, 1, 2 , 3, 4, 5, 6, 8, 9.
We will train the autoencoder on the normal dataset and then apply it to the test set. The aim will be to abstract as many features of the normal situation as possible. This requires knowledge of the normal situation, which translates into availability of labelled data and hence, it is an ideal scenario, for many practical applications, for instance credit card ...
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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.