January 2019
Intermediate to advanced
386 pages
11h 13m
English
book
Python Deep Learning - Second Edition
by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Content preview from Python Deep Learning - Second Edition
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Summary
In this chapter, we explained what deep learning is and how it's related to deep neural networks. We discussed the different types of networks and how to train them. We also mentioned many real-world applications of deep learning and tried to analyze the reasons for its efficiency. Finally, we introduced three of the most popular deep learning libraries, namely, TensorFlow, Keras and PyTorch. We also implemented a couple of examples with Keras, but we hit a low accuracy ceiling when we tried to classify the CIFAR-10 dataset.
In the next chapter, we'll discuss how to improve these results with the help of convolutional networks – one of the most popular and effective deep network models. We'll talk about their structure, building blocks, ...
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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.