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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Experience replay
As we discussed in the Value function approximation section, we are training the network online, as the agent receives stream of experiences from the environment. But the environment is usually sequential, and consecutive experiences might not differ much. For example, imagine that the agent is a car, which is currently sliding downhill. While doing so, it receives consistent feedback that the speed increases. If we feed the network with such unified training data, there is a chance that it will start dominating all other experiences. The network might "forget" previous situations and the performance would decrease (this is a disadvantage of some neural networks). We can solve this issue with experience replay. As the environment ...
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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.