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文件名称：Learning generative adversarial networks : next-generation deep learning
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更新时间：2021-11-16 10:33:32
对抗神经网络
Chapter 1, Introduction to Deep Learning, speaks all about refreshing general concepts and
terminology associated with deep learning in a simple way without too much math and
equations. Also, it will show how deep learning network has evolved throughout the years and
how they are making an inroad in the unsupervised domain with the emergence of generative
models.
Chapter 2, Unsupervised Learning with GAN, shows how Generative Adversarial Networks
work and speaks about the building blocks of GANs. It will show how deep learning networks
can be used on semi-supervised domains, and how you can apply them to image generation
and creativity. GANs are hard to train. This chapter looks at some techniques to improve the
training/learning process.
Chapter 3, Transfer Image Style Across Various Domains, speaks about being very creative
with simple but powerful CGAN and CycleGAN models. It explains the use of Conditional GAN
to create images based on certain characteristics or conditions. This chapter also discusses
how to overcome model collapse problems by stabilizing your network training using BEGAN.
And finally, it covers transferring styles across different domains (apple to orange; horse to
zebra) using CycleGAN.
Chapter 4, Building Realistic Images from Your Text, presents the latest approach of stacking
Generative Adversarial Networks into multiple stages to decompose the problem of text to
image synthesis into two more manageable subproblems with StackGAN. The chapter also
shows how DiscoGAN successfully transfers styles across multiple domains to generate
output images of handbags from the given input of shoe images or to perform gender
transformations of celebrity images.
Chapter 5, Using Various Generative Models to Generate Images, introduces the concept of a
pretrained model and discusses techniques for running deep learning and generative models
over large distributed systems using Apache Spark. We will then enhance the resolution of low
quality images using pretrained models with GAN. And finally, we will learn other varieties of
generative models such as DeepDream and VAE for image generation and styling.
Chapter 6, Taking Machine Learning to Production, describes various approaches to deploying
machine learning and deep learning-based intelligent applications to production both on data
center and the cloud using microservice-based containerized or serverless techniques.