Item #:
142420330
Neural Networks and Deep Learning: A Textbook
86% of respondents would recommend this to a friend
Item #:
142420330
SLE 2574
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The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks.
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What Stands Out
Comprehensive Coverage
This textbook provides a thorough introduction to neural networks and deep learning, covering foundational concepts to advanced topics, making it suitable for both beginners and seasoned professionals.
Practical Applications
Real-world examples and applications throughout the book demonstrate how neural networks can be utilized across various industries, bridging the gap between theory and practice for readers.
Updated Content
With the latest advancements in deep learning techniques and technologies, this textbook ensures readers stay current, reflecting the evolving landscape of artificial intelligence.
Product Details
- 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.
| Publisher | Springer |
| Publication date | 30 Jun. 2023 |
| Edition | Second Edition 2023 |
| Language | English |
| Print length | 553 pages |
| ISBN-10 | 3031296419 |
| ISBN-13 | 978-3031296413 |
| Dimensions | 17.78 x 3.02 x 25.4 cm |
Who Should Buy?
-
Students
Ideal for undergraduate and graduate students seeking a foundational understanding of neural networks and deep learning concepts.
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Professionals
Recommended for data scientists and software developers wanting to enhance their skills in machine learning and AI technologies.
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Academics
Perfect for educators and researchers looking for a comprehensive textbook to support teaching and academic studies in AI.
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Beginner Users
Not suitable for absolute beginners without prior knowledge of programming or basic machine learning principles.
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English edition Charu C. Aggarwal Format: Hardcover Editorial Review
Neural Networks And Deep Learning: A Textbook offers a comprehensive insight into machine learning algorithms and theories. This second edition, published by Springer on June 30, 2023, spans 553 pages, making it a thorough guide for both beginners and experienced practitioners. It is available in English and provides an extensive overview of neural networks, featuring detailed explanations and practical examples. The book also covers the latest advancements in the field, making it an essential resource for anyone looking to deepen their understanding of deep learning techniques. Particularly well-received for its clear writing style, it serves as a critical tool for students and professionals alike.
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Pros
- Comprehensive coverage of neural network concepts
- Clear explanations and practical examples
- Suitable for beginners and experienced users
- Latest advancements in the field included
- Well-structured for easy navigation
Cons
- Some sections may require prior knowledge of mathematics
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Features & Benefits
- Covers both classical and modern models in deep learning
- Focus on theory and algorithms of deep learning
- Rich discussions on different applications of neural architectures
- Detailed coverage of deep learning methods for text, images, and graphs
- Revised edition with greater focus on modern deep learning ideas
- Targeted towards graduate and upper undergraduate level students, as well as researchers and practitioners
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