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Hands-On Neural Networks

You're reading from   Hands-On Neural Networks Learn how to build and train your first neural network model using Python

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788992596
Length 280 pages
Edition 1st Edition
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Authors (2):
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Leonardo De Marchi Leonardo De Marchi
Author Profile Icon Leonardo De Marchi
Leonardo De Marchi
Laura Mitchell Laura Mitchell
Author Profile Icon Laura Mitchell
Laura Mitchell
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started FREE CHAPTER
2. Getting Started with Supervised Learning 3. Neural Network Fundamentals 4. Section 2: Deep Learning Applications
5. Convolutional Neural Networks for Image Processing 6. Exploiting Text Embedding 7. Working with RNNs 8. Reusing Neural Networks with Transfer Learning 9. Section 3: Advanced Applications
10. Working with Generative Algorithms 11. Implementing Autoencoders 12. Deep Belief Networks 13. Reinforcement Learning 14. Whats Next? 15. Other Books You May Enjoy

StyleGAN

StyleGAN is a GAN design released by researchers at NVIDIA in December 2018. It is essentially an upgraded version of ProGAN. It combined ProGAN with neural style transfer. At the core of the StyleGAN architecture is a style-transfer technique. The model set a new record for face generation tasks and can also be used to generate realistic images of cars, bedrooms, houses, and so on.

As with ProGAN, StyleGAN generates images gradually by starting with a very low resolution and continuing to a high-resolution image. The GAN controls the visual features that are expressed in each level, from coarse features such as the pose and face shape, through to the finer features such as eye and hair color:

The source for this image can be found at: https://arxiv.org/abs/1812.04948

The generator in StyleGAN incorporates a mapping network. The goal of the mapping network is to encode...

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