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    Hands-On Generative Adversarial Networks with PyTorch 1.x. Implement next-generation neural networks to build powerful GAN models using Python

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    John Hany, Greg Walters
    Hands-On Generative Adversarial Networks with PyTorch 1.x. Implement next-generation neural networks to build powerful GAN models using Python John Hany, Greg Walters - okladka książki

    Hands-On Generative Adversarial Networks with PyTorch 1.x. Implement next-generation neural networks to build powerful GAN models using Python John Hany, Greg Walters - okladka książki

    Hands-On Generative Adversarial Networks with PyTorch 1.x. Implement next-generation neural networks to build powerful GAN models using Python John Hany, Greg Walters - audiobook MP3

    Hands-On Generative Adversarial Networks with PyTorch 1.x. Implement next-generation neural networks to build powerful GAN models using Python John Hany, Greg Walters - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    312
    Dostępne formaty:
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    Ebook (29,90 zł najniższa cena z 30 dni)

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    Do przechowalni

    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 starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models.
    By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems.

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    O autorach książki

    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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