neural networks deep learning von aggarwal charu (28 Ergebnisse)

Hardcover. Zustand: Very Good. 1st ed. 2018. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.

hardcover. Zustand: Acceptable. Some damage to the hindges causing the cover to be slightly off compaired to the pages.

Hardcover. Zustand: Good. 1st ed. 2018. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.

Zustand: Gut. Zustand: Gut | Seiten: 553 | Sprache: Englisch | Produktart: Bücher.

hardcover. Zustand: Sehr gut. 520 Seiten; 9783319944623.2 Gewicht in Gramm: 2.

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

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Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1150grams, ISBN:9783030068561.

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

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.

Taschenbuch. Zustand: Neu. Neuware -This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work When do they work better than off-the-shelf machine-learning models When is depth useful Why is training neural networks so hard What are the pitfalls The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories:The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2.Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks.Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines.Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12.The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 556 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work When do they work better than off-the-shelf machine-learning models When is depth useful Why is training neural networks so hard What are the pitfalls The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories:The basics of neural networks:The backpropagation algorithm is discussed in Chapter 2.Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks.Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines.Advanced topics in neural networks: Chapters 8, 9, and 10 discussrecurrent neural networks, convolutional neural networks, and graph neural networks.Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12.The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.

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

Zustand: New. In.

Zustand: New.

Zustand: NEW.

Paperback. Zustand: Brand New. 2nd edition. 553 pages. 10.00x7.00x10.00 inches. In Stock.

Hardcover. Zustand: Very Good. No Jacket. 1st Edition. Very good+ hardcover with clean & bright boards w/minor bumps/dents to boards; interior pages are white & crisp, no marks, looks barely used if at all. No dust jacket as issued. See photos.

Zustand: New. Simple and intuitive discussions of neural networks and deep learningProvides mathematical details without losing the reader in complexityIncludes exercises and examplesDiscusses both traditional neural networks and recent deep learn.

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

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.

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

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work When do they work better than off-the-shelf machine-learning models When is depth useful Why is training neural networks so hard What are the pitfalls The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories:The basics of neural networks:The backpropagation algorithm is discussed in Chapter 2.Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks.Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines.Advanced topics in neural networks: Chapters 8, 9, and 10 discussrecurrent neural networks, convolutional neural networks, and graph neural networks.Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12.The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.

Zustand: New. In.

Zustand: New.

Hardcover. Zustand: Brand New. 497 pages. 10.00x7.25x1.25 inches. In Stock.