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Hands-On Generative Adversarial Networks with PyTorch 2.x. Gain hands-on expertise in utilizing robust Generative AI models to tackle a wide array of challenges - Second Edition

Język publikacji: angielskim
Autor:
Marija Jegorova
Hands-On Generative Adversarial Networks with PyTorch 2.x. Gain hands-on expertise in utilizing robust Generative AI models to tackle a wide array of challenges - Second Edition Marija Jegorova - okladka książki

Hands-On Generative Adversarial Networks with PyTorch 2.x. Gain hands-on expertise in utilizing robust Generative AI models to tackle a wide array of challenges - Second Edition Marija Jegorova - okladka książki

Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Generative AI is the most spoken of AI direction in media nowadays, and this book is aimed at assisting you in becoming an expert in its most well-established class of models - Generative Adversarial Nets.



With the help of this book, you will work your way up from understanding the basic components and architecture of GANs, building your first model from scratch to designing, building, training and optimizing a wide variety of these powerful models. You will go way beyond theoretical knowledge and gain hands-on experience in finding the right type of GAN for each specific problem using PyTorch examples provided in every chapter.



You will cover important image-generation and translation architectures such as classic and conditional GANs, DCGANs, StyleGANs, CycleGANs, and pix2pix. Learn to synthesize sequences, text and audio, and generate videos. Finally, we will dive into the state-of-the-art hybrid models of GANs with other generative models.



By the end of this book, you will be an expert in practical applications of GANs to real-world problems.

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O autorze książki

Marija currently holds the position of a Senior Machine Learning Research Scientist at Metaphysic.ai. She has worked in Generative AI research for over 8 years, 5+ of them specifically with GANs. Previous employers and projects include Meta, iCAIRD (NHS Scotland), Seebyte (Batelle Company), and DREAM (EU Horizon2020). Her PhD is in Generative Models Applications to Robotics and Automation, acquired from the University of Edinburgh. She also has an MSc in Computational Statistics and Machine Learning from the University College London. She has authored multiple academic publications in several prestigious peer-reviewed venues in the fields of Machine Learning and Robotics, such as IEEE IROS, ICRA, and TPAMI.

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