Neural networks and deep learning : a textbook
Material type:
TextPublication details: Cham : Springer, 2023. Edition: 2nd editionDescription: xxiv, 529 pages. : illustrations (128 b/w illustrations, 22 illustrations in colour)ISBN: 9783031296413Subject(s): Neural networks (Computer science) | Deep learning | Machine learning | Artificial intelligence | Pre-trained language models | Reinforcement learning | Recurrent neural networksDDC classification: 004.032.26 Online resources: Table of content | Reviews Summary: 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.
| Item type | Current library | Call number | Status | Date due | Barcode |
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Book
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NISER LIBRARY | 004.032.26 AGG-N (Browse shelf(Opens below)) | Available | 25768 |
Includes bibliographical references and index
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.
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