SPRI | Neural Networks And Deep Learning: A Textbook, 2nd Edition (2023 EN)

Author: Charu C. Aggarwal
Full Title: Neural Networks And Deep Learning: A Textbook, 2nd Edition
Publisher: Springer; 2nd ed. 2023 edition (June 30, 2023)
Year: 2023
ISBN-13: 9783031296420 (978-3-031-29642-0), 9783031296413 (978-3-031-29641-3), 9783031296444 (978-3-031-29644-4)
ISBN-10: 3031296427, 3031296419, 3031296443
Pages: 529
Language: English
Genre: IT: Deep Learning
File type: EPUB (True), PDF (True)
Quality: 10/10
Price: 74.89 €


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.


Overview:
✓ Simple and intuitive discussions of neural networks and deep learning
✓ Provides mathematical details without losing the reader in complexity
✓ Includes exercises and examples
✓ Discusses both traditional neural networks and recent deep learning models

Readership:
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.

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