Learning generative adversarial networks : next-generation deep learning simplified PDF
Preview Learning generative adversarial networks : next-generation deep learning simplified
Table of Contents Learning Generative Adversarial Networks Credits About the Author About the Reviewer www.PacktPub.com eBooks, discount offers, and more Why subscribe? Customer Feedback Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Introduction to Deep Learning Evolution of deep learning Sigmoid activation Rectified Linear Unit (ReLU) Exponential Linear Unit (ELU) Stochastic Gradient Descent (SGD) Learning rate tuning Regularization Shared weights and pooling Local receptive field Convolutional network (ConvNet) Deconvolution or transpose convolution Recurrent Neural Networks and LSTM Deep neural networks Discriminative versus generative models Summary 2. Unsupervised Learning with GAN Automating human tasks with deep neural networks The purpose of GAN An analogy from the real world The building blocks of GAN Generator Discriminator Implementation of GAN Applications of GAN Image generation with DCGAN using Keras Implementing SSGAN using TensorFlow Setting up the environment Challenges of GAN models Setting up failure and bad initialization Mode collapse Problems with counting Problems with perspective Problems with global structures Improved training approaches and tips for GAN Feature matching Mini batch Historical averaging One-sided label smoothing Normalizing the inputs Batch norm Avoiding sparse gradients with ReLU, MaxPool Optimizer and noise Don't balance loss through statistics only Summary 3. Transfer Image Style Across Various Domains Bridging the gap between supervised and unsupervised learning Introduction to Conditional GAN Generating a fashion wardrobe with CGAN Stabilizing training with Boundary Equilibrium GAN The training procedure of BEGAN Architecture of BEGAN Implementation of BEGAN using Tensorflow Image to image style transfer with CycleGAN Model formulation of CycleGAN Transforming apples into oranges using Tensorflow Transfiguration of a horse into a zebra with CycleGAN Summary 4. Building Realistic Images from Your Text Introduction to StackGAN Conditional augmentation Stage-I Stage-II Architecture details of StackGAN Synthesizing images from text with TensorFlow Discovering cross-domain relationships with DiscoGAN The architecture and model formulation of DiscoGAN Implementation of DiscoGAN Generating handbags from edges with PyTorch Gender transformation using PyTorch DiscoGAN versus CycleGAN Summary 5. Using Various Generative Models to Generate Images Introduction to Transfer Learning The purpose of Transfer Learning Various approaches of using pre-trained models Classifying car vs cat vs dog vs flower using Keras Large scale deep learning with Apache Spark Running pre-trained models using Spark deep learning Handwritten digit recognition at a large scale using BigDL High resolution image generation using SRGAN Architecture of the SRGAN 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 6. Taking Machine Learning to Production Building an image correction system using DCGAN Steps for building an image correction system Challenges of deploying models to production Microservice architecture using containers Drawbacks of monolithic architecture Benefits of microservice architecture Containers Docker Kubernetes Benefits of using containers Various approaches to deploying deep models Approach 1 - offline modeling and microservice-based containerized deployment Approach 2 - offline modeling and serverless deployment Approach 3 - online learning Approach 4 - using a managed machine learning service 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 Steps to modify code and packages for lambda environments Running face detection with a cloud managed service Summary Index Learning Generative Adversarial Networks Learning Generative Adversarial Networks Copyright © 2017 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: October 2017 Production reference: 1241017 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78839-641-7 www.packtpub.com Credits Author Kuntal Ganguly Reviewer Max Strakhov Commissioning Editor Amey Varangaonkar Acquisition Editor Divya Poojari Content Development Editor Dattatraya More Technical Editor Jovita Alva Copy Editor Safis Editing Project Coordinator Shweta H Birwatkar Proofreader Safis Editing Indexer Rekha Nair Graphics Tania Dutta Production Coordinator Shantanu Zagade Cover Work Shantanu Zagade About the Author Kuntal Ganguly is a big data analytics engineer focused on building large-scale, data-driven systems using big data frameworks and machine learning. He has around 7 years experience of building big data and machine learning applications. Kuntal provides solutions to cloud customers in building real-time analytics systems using managed cloud services and open source Hadoop ecosystem technologies such as Spark, Kafka, Storm, Solr, and so on, along with machine learning and deep learning frameworks. Kuntal enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several large-scale distributed applications. He is a machine learning and deep learning practitioner and is very passionate about building intelligent applications. His LinkedIn profile is as follows: https://in.linkedin.com/in/kuntal-ganguly-59564088 I am grateful to my mother, Chitra Ganguly, and father, Gopal Ganguly, for their love, support, and for teaching me a lot about hard work—even the little I have absorbed has helped me immensely throughout my life. I would also like to thank all my friends, colleagues, and mentors that I've had over the years. About the Reviewer Max Strakhov is a research and software engineer with over 8 years experience in computer programming and over 4 years experience in machine learning. He has worked at Google and Yandex and is a cofounder and a CTO of AURA Devices LLC. He is interested in deep learning and its applications in artificial intelligence, especially in generative modelling. He has a blog, monnoroch.github.io, where he shares posts about deep learning, software engineering, and all things related to technology. www.PacktPub.com eBooks, discount offers, and more Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.PacktPub.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at <[email protected]> for more details. 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