Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras by Josh Kalin (PDF)

Description

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and KerasKey FeaturesUnderstand the common architecture of different types of GANs Train, optimize, and deploy GAN applications using TensorFlow and Keras Build generative models with real-world data sets, including 2D and 3D dataBook DescriptionDeveloping Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.What you will learnStructure a GAN architecture in pseudocode Understand the common architecture for each of the GAN models you will build Implement different GAN architectures in TensorFlow and Keras Use different datasets to enable neural network functionality in GAN models Combine different GAN models and learn how to fine-tune them Produce a model that can take 2D images and produce 3D models Develop a GAN to do style transfer with Pix2PixWho this book is forThis book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.Table of ContentsWhat is a Generative Adversarial Network? Data First – How to prepare your datasetMy First GAN in under 100 linesDreaming new Kitchens using DCGANPix2Pix Image-to-Image TranslationStyle Transfering Your image using CycleGANUse Simulated Images to Create Photo Realistic Eyeballs using simGANFrom Image to 3D Models using GANs

User’s Reviews

Editorial Reviews: About the Author Josh Kalin is a Physicist and Technologist focused on the intersection of robotics and machine learning. Josh works on advanced sensors, industrial robotics, machine learning, and automated vehicle research projects. Josh holds degrees in Physics, Mechanical Engineering, and Computer Science. In his free time, he enjoys working on cars (has owned 36 vehicles and counting), building computers, and learning new techniques in robotics and machine learning (like writing this book).

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐A lot of none sense printed in big fonts “How to do it” “Get ready”. Legend are all in very tiny small font. He likes to say “here is how to do it” Then show part of codes. Then say “now you know how to do it.” He also likes to waste readers’ time by saying “Do you think I will stop here? No….” This book is watery. Not substantial. Figures are crapy. Some figures are corrupted and hard to read. Some scatter plots ought to be printed with different markers, o, x , square, because it’s a black and white book. But author just print three groups of markers all in circle but with different color. Which makes readingthe figure impossible from the printed book. You can go to github to download figures. But it’s so stupid. The listings of the codes wasted a lot of space on import packages and set up shells. The key part of the code are often omitted. Some genereated figures by GAN are shown as a block of grey image. Author does not even bother to generate a figure to be used in his book. But author spend time to mention he owned 36 cars. Anyway, I found Another book by Rowel Atienza is much more readable and helpful. I regret so much buying this book. Don’t buy it.

⭐I’m a full time software developer who is looking to build some GANs to learn and this book was exactly what I was looking for. Breaking down the code and referencing the original papers is perfect, especially since I do not know python extremely well.From here I plan on buying a couple other ML books and then I would like to learn more about the math details but this book got me started on the right foot for me to go find out answers to all/most of my questions.

⭐This book was very insightful and I am glad that there are such niece books available to learn about emerging ML and AI technologies.

⭐I have nothing to say, it does not worth your money

Keywords

Free Download Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras in PDF format
Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras PDF Free Download
Download Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras 2018 PDF Free
Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras 2018 PDF Free Download
Download Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras PDF
Free Download Ebook Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras