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Название: Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, 2nd Edition (Final)
Автор: David Foster
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 456
Язык: английский
Формат: True PDF, True EPUB (Retail Copy)
Размер: 86.5 MB
Generative AI is one of the most revolutionary technologies of our time, transforming the way we interact with machines. Its potential to revolutionize the way we live, work, and play has been the subject of countless conversations, debates, and predictions. But what if there was an even greater potential to this powerful technology? What if the possibilities of generative AI extend beyond our current imagination? The future of generative AI may be more exciting than we ever thought possible… Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models such as variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy based models, and diffusion models.
Автор: David Foster
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 456
Язык: английский
Формат: True PDF, True EPUB (Retail Copy)
Размер: 86.5 MB
Generative AI is one of the most revolutionary technologies of our time, transforming the way we interact with machines. Its potential to revolutionize the way we live, work, and play has been the subject of countless conversations, debates, and predictions. But what if there was an even greater potential to this powerful technology? What if the possibilities of generative AI extend beyond our current imagination? The future of generative AI may be more exciting than we ever thought possible… Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models such as variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy based models, and diffusion models.