December 2019
Intermediate to advanced
468 pages
14h 28m
English
Content preview from Advanced Deep Learning with Python
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Summary
We started this chapter with a quick recap of CNNs and discussed transposed, depthwise separable, and dilated convolutions. Next, we talked about improving the performance of CNNs by representing the convolution as a matrix multiplication or with the Winograd convolution algorithm. Then, we focused on visualizing CNNs with the help of guided backpropagation and Grad-CAM. Next, we discussed the most popular regularization techniques. Finally, we learned about transfer learning and implemented the same TL task with both PyTorch and TF as a way to compare the two libraries.
In the next chapter, we'll discuss some of the most popular advanced CNN architectures.
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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.