July 2018
Intermediate to advanced
96 pages
2h 8m
English
Content preview from Hands-On Deep Learning for Images with TensorFlow
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Hyperparameters
In this section, we'll explore hyperparameters, or parameters that can't quite be machine learned.
We'll also cover trainable parameters (these are the parameters that are learned by the solver), nontrainable parameters (additional parameters in the models that don't require training), and then finally, hyperparameters (parameters that aren't learned by a traditional solver).
In our Model summary output screenshot, pay attention to the number of trainable parameters in the highlighted section of code at the bottom of the screenshot. That is the number of individual floating-point numbers that are contained inside of our model that our adam optimizer, in conjunction with our categorical cross-entropy loss function, will be ...
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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.