Learning Generative Adversarial Networks

Build image generation and semi-supervised models using Generative Adversarial Networks
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Learning Generative Adversarial Networks

Kuntal Ganguly

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Build image generation and semi-supervised models using Generative Adversarial Networks

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Book Details

ISBN 139781788396417
Paperback180 pages

Book Description

Generative models are gaining a lot of popularity among the data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically builds an understanding of it. Unlike supervised learning methods, generative models do not require labeling of the data which makes it an interesting system to use. This book will help you to build and analyze the deep learning models and apply them to real-world problems. This book will help readers develop intelligent and creative application from a wide variety of datasets, mainly focusing on visuals or images.

The book begins with the basics of generative models, as you get to know the theory behind Generative Adversarial Networks and its building blocks. This book will show you how you can overcome the problem of text to image synthesis with GANs, using libraries like Tensorflow, Keras and PyTorch. Transfering style from one domain to another becomes a headache when working with huge data sets. The author, using real-world examples, will show how you can overcome this. You will understand and train Generative Adversarial Networks and use them in a production environment and learn tips to use them effectively and accurately.

Table of Contents

Chapter 1: Introduction to Deep Learning
Evolution of deep learning
Deconvolution or transpose convolution
Summary
Chapter 2: Unsupervised Learning with GAN
Automating human tasks with deep neural networks
Implementation of GAN
Challenges of GAN models
Improved training approaches and tips for GAN
Summary
Chapter 3: Transfer Image Style Across Various Domains
Bridging the gap between supervised and unsupervised learning
Introduction to Conditional GAN
The training procedure of BEGAN
Image to image style transfer with CycleGAN
Summary
Chapter 4: Building Realistic Images from Your Text
Introduction to StackGAN
Discovering cross-domain relationships with DiscoGAN
Generating handbags from edges with PyTorch
Gender transformation using PyTorch
DiscoGAN versus CycleGAN
Summary
Chapter 5: Using Various Generative Models to Generate Images
Introduction to Transfer Learning
Large scale deep learning with Apache Spark
Generating artistic hallucinated images using DeepDream
Generating handwritten digits with VAE using TensorFlow
A real world analogy of VAE
A comparison of two generative models—GAN and VAE
Summary
Chapter 6: Taking Machine Learning to Production
Building an image correction system using DCGAN
Microservice architecture using containers
Various approaches to deploying deep models
Serving Keras-based deep models on Docker
Deploying a deep model on the cloud with GKE
Serverless image recognition with audio using AWS Lambda and Polly
Summary

What You Will Learn

  • Understand the basics of deep learning and the difference between discriminative and generative models
  • Generate images and build semi-supervised models using Generative Adversarial Networks (GANs) with real-world datasets.
  • Tune GAN models by addressing the challenges such as mode collapse, training instability using mini batch, feature matching, and the boundary equilibrium technique.
  • Use stacking with Deep Learning architectures to run and generate images from text.
  • Couple multiple Generative models to discover relationships across various domains
  • Explore the real-world steps to deploy deep models in production

Authors

Table of Contents

Chapter 1: Introduction to Deep Learning
Evolution of deep learning
Deconvolution or transpose convolution
Summary
Chapter 2: Unsupervised Learning with GAN
Automating human tasks with deep neural networks
Implementation of GAN
Challenges of GAN models
Improved training approaches and tips for GAN
Summary
Chapter 3: Transfer Image Style Across Various Domains
Bridging the gap between supervised and unsupervised learning
Introduction to Conditional GAN
The training procedure of BEGAN
Image to image style transfer with CycleGAN
Summary
Chapter 4: Building Realistic Images from Your Text
Introduction to StackGAN
Discovering cross-domain relationships with DiscoGAN
Generating handbags from edges with PyTorch
Gender transformation using PyTorch
DiscoGAN versus CycleGAN
Summary
Chapter 5: Using Various Generative Models to Generate Images
Introduction to Transfer Learning
Large scale deep learning with Apache Spark
Generating artistic hallucinated images using DeepDream
Generating handwritten digits with VAE using TensorFlow
A real world analogy of VAE
A comparison of two generative models—GAN and VAE
Summary
Chapter 6: Taking Machine Learning to Production
Building an image correction system using DCGAN
Microservice architecture using containers
Various approaches to deploying deep models
Serving Keras-based deep models on Docker
Deploying a deep model on the cloud with GKE
Serverless image recognition with audio using AWS Lambda and Polly
Summary

Book Details

ISBN 139781788396417
Paperback180 pages
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