May 2019
Intermediate to advanced
272 pages
7h 19m
English
Content preview from Hands-On Generative Adversarial Networks with Keras
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Generation of Discrete Sequences Using GANs
In this chapter, you will learn how to implement a model that is used in the paper Adversarial Generation of Natural Language by Rajeswar et al. This model was first described in the paper Improved Training of Wasserstein GANs by Gulrajani et al. It is capable of generating short discrete sequences with small vocabularies.
We will first address language generation as a problem of conditional probability, in which we want to estimate the probability of the next token given the previous tokens. Then will address the challenges involved in training models for discrete sequences using GANs.
After this introduction to language generation, you will learn how to implement the model described in the paper ...
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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.