9783031296413 - Neural Networks and Deep Learning: a Textbook by Aggarwal, Charu C. (33 results)
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Seller: medimops, Berlin, Germanymedimops
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Hardcover. Condition: Very Good. Second Edition 2023. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
- Hardcover
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- Hardcover
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- Hardcover
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Language: English
Published by Springer International Publishing AG, Cham 2023
- Hardcover
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Hardcover. Condition: new. Hardcover. 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 desig…n 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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- Hardcover
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Language: English
Published by Springer International Publishing AG, CH 2023
- Hardcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Hardback. Condition: New. Second Edition 2023.
- Hardcover
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- Hardcover
Seller: Brook Bookstore, Milano, MI, ItalyBrook Bookstore
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Language: English
Published by Springer International Publishing AG, CH 2023
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Hardback. Condition: New. Second Edition 2023.
- Hardcover
Seller: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, , GermanyRheinberg-Buch Andreas Meier eK
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Buch. Condition: 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 conce…pts 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. 556 pp. Englisch.
- Hardcover
Seller: Speedyhen, Hertfordshire, United KingdomSpeedyhen
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- Hardcover
Seller: moluna, Greven, , Germanymoluna
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Condition: 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.
Language: English
Published by Springer International Publishing AG, CH 2023
- Hardcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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Hardback. Condition: New. Second Edition 2023.
More images- Hardcover
Seller: preigu, Osnabrück, Germanypreigu
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Buch. Condition: Neu. Neural Networks and Deep Learning | A Textbook | Charu C. Aggarwal | Buch | xxiv | Englisch | 2023 | Springer | EAN 9783031296413 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: 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 o…ne 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.
Language: English
Published by Springer International Publishing AG, Cham 2023
- Hardcover
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Hardcover. Condition: new. Hardcover. 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 desig…n 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Language: English
Published by Springer International Publishing AG, CH 2023
- Hardcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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Hardback. Condition: New. Second Edition 2023.
- Hardcover
- Print on Demand
Seller: Revaluation Books, Exeter, , United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 2nd edition. 553 pages. 10.00x7.01x1.38 inches. In Stock. This item is printed on demand.
