ISBN: 9781484289303 - pro deep learning with tensorflow 2.0: a mathematical approach to advanced artificial intelligence in python (12 Ergebnisse)

paperback. Zustand: Very Good. Cover and edges may have some wear.

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

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

Zustand: New. In English.

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.

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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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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: 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.…