Pro deep learning tensorflow von santanu pattanayak (20 Ergebnisse)

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

paperback. Zustand: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

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Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. In English.

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Zustand: New. 2023. 2nd ed. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 2nd edition. 672 pages. 10.00x7.01x1.42 inches. In Stock.

Kartoniert / Broschiert. Zustand: New. This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.Pro Deep Learning with TensorFlow 2.0 begins with the mathematical .

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Paperback. Zustand: Brand New. 398 pages. 9.75x6.75x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Pro Deep Learning with TensorFlow 2.0 | A Mathematical Approach to Advanced Artificial Intelligence in Python | Santanu Pattanayak | Taschenbuch | xx | Englisch | 2023 | Apress | EAN 9781484289303 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 672 | Sprache: Englisch | Produktart: Bücher | This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. Yoüll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, yoüll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE.Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications.What You Will LearnUnderstand full-stack deep learning using TensorFlow 2.0Gain an understanding of the mathematical foundations of deep learning Deploy complex deep learning solutions in production using TensorFlow 2.0Understand generative adversarial networks, graph attention networks, and GraphSAGEWho This Book Is For: Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.…

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 672 | Sprache: Englisch | Produktart: Bücher | This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. Yoüll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, yoüll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE.Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications.What You Will LearnUnderstand full-stack deep learning using TensorFlow 2.0Gain an understanding of the mathematical foundations of deep learning Deploy complex deep learning solutions in production using TensorFlow 2.0Understand generative adversarial networks, graph attention networks, and GraphSAGEWho This Book Is For: Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.…