Generative Adversarial Networks Projects, published by Packt
This is the code repository for Generative-Adversarial-Networks-Projects, published by Packt.
Build next-generation generative models using TensorFlow and Keras
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:
If you feel this book is for you, get your copy today!
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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).
| 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) |
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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