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Generative-Adversarial-Networks-Projects

Generative Adversarial Networks Projects, published by Packt

Generative-Adversarial-Networks-Projects

This is the code repository for Generative-Adversarial-Networks-Projects, published by Packt.

Build next-generation generative models using TensorFlow and Keras

What is this book about?

Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.

This book covers the following exciting features:

Train a network on the 3D ShapeNet dataset to generate realistic shapes
Generate anime characters using the Keras implementation of DCGAN
Implement an SRGAN network to generate high-resolution images
Train Age-cGAN on Wiki-Cropped images to improve face verification
Use conditional GANs for image-to-image translation

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

import scipy.io as io
voxels = io.loadmat("path to .mat file")[ 'instance' ]

Following is what you need for this book: If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.

With the following software and hardware list you can run all code files present in the book (Chapter 1-09).

Software and Hardware List

Chapter	Software required	OS required
1	Python 3.5	Windows, Mac OS X, and Linux (Any)
2	AWS	Windows, Mac OS X, and Linux (Any)
3	GPU	Windows, Mac OS X, and Linux (Any)

Get to Know the Author(s)

Kailash Ahirwar Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.

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