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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Character-based models for generating new text
In this section, we'll discuss how to generate new text using character-based models via TensorFlow (TF). This is an example of a "many-to-many" relationship, such as the one we defined in the Recurrent neural networks section. We'll only discuss the most interesting code sections, but the full example lives at https://github.com/ivan-vasilev/Python-Deep-Learning-SE/tree/master/ch07/language%20model.
In most cases, language modeling is performed at the word level, where the distribution is over a fixed vocabulary of |V| words. Vocabularies in realistic tasks, such as the language models used in speech recognition, often exceed 100,000 words. This large dimensionality makes modeling the output ...
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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.