May 2018
Intermediate to advanced
576 pages
14h 42m
English
Content preview from Mastering Machine Learning Algorithms
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Generative Adversarial Networks
In this chapter, we are going to provide a brief introduction to a family of generative models based on some game theory concepts. Their main peculiarity is an adversarial training procedure that is aimed at learning to distinguish between true and fake samples, driving, at the same time, another component that generates samples more and more similar to the training examples.
In particular, we will be discussing:
- Adversarial training and standard Generative Adversarial Networks (GANs)
- Deep Convolutional GANs (DCGAN)
- Wasserstein GANs (WGAN)
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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.