October 2018
Intermediate to advanced
472 pages
10h 57m
English
Content preview from Python Deep Learning Projects
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Conclusion
This project was all about building a CNN classifier to classify handwritten digits better than we did in Chapter 2, Training NN for Prediction Using Regression, with a multilayer Perceptron.
Our deep convolution neural network classifier with max pooling and dropout hit 99.01% accuracy on a test set of 10,000 images/digits. This is good. This is almost 12% better than our multilayer Perceptron model.
However, there are some implications. What are the implications of this accuracy? It is important that we understand this. Just like we did in Chapter 2, Training NN for Prediction Using Regression, let's calculate the incidence of an error occurring that would result in a customer service issue.
Just to refresh our memory, in this ...
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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.