December 2017
Intermediate to advanced
536 pages
14h 23m
English
Content preview from TensorFlow 1.x Deep Learning Cookbook
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A working example - MNIST
The working example is based on the Github page: https://github.com/ywpkwon/siamese_tf_mnist. This code here uses Siamese Networks to embed hand-written MNIST digits into 2D space, digits belonging to same class are embedded together. The code consists of three major files:
● run.py: It contains the basic wrapper to perform training. It uses the Gradient Descent algorithm to minimize the contrastive loss.
● inference.py: This contains the Siamese Class which defines a 3-layered fully connected network. The similarity between the output of two networks in the code is Euclidean. The partial generative loss and the partial imposter Loss are used to then to calculate the Contrastive loss.
● visualize.py: This is again ...
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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.