This is the code repository for Hands-On Generative Adversarial Networks with PyTorch 1.0 , published by Packt.

Implement next-generation neural networks to build powerful GAN models using Python

With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples.

This book covers the following exciting features: Implement PyTorch's latest features to ensure efficient model designing Get to grips with the working mechanisms of GAN models Perform style transfer between unpaired image collections with CycleGAN Build and train 3D-GANs to generate a point cloud of 3D objects Create a range of GAN models to perform various image synthesis operations Use SEGAN to suppress noise and improve the quality of speech audio

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

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

The code will look like the following:

    # Derivative with respect to w3
    d_w3 = np.matmul(np.transpose(self.x2), delta)
    # Derivative with respect to b3
    d_b3 = delta.copy()

Following is what you need for this book: This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You’ll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Generative Adversarial Networks Projects [Packt] [Amazon]

• Generative Adversarial Networks Cookbook [Packt] [Amazon]

John Hany received his master's degree and bachelor's degree in calculational mathematics at the University of Electronic Science and Technology of China. He majors in pattern recognition and has years of experience in machine learning and computer vision. He has taken part in several practical projects, including intelligent transport systems and facial recognition systems. His current research interests lie in reducing the computation costs of deep neural networks while improving their performance on image classification and detection tasks. He is enthusiastic about open source projects and has contributed to many of them.

Greg Walters has been involved with computers and computer programming since 1972. He is well-versed in Visual Basic, Visual Basic .NET, Python, and SQL and is an accomplished user of MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous people on many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is semi-retired and has written over 100 articles for Full Circle Magazine. He is also a musician and loves to cook. He is open to working as a freelancer on various projects.

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