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Neural Networks and Deep Learning: A Textbook by Charu - Paperback NEW Charu C.

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Last updated on 27 Apr, 2024 14:28:15 BSTView all revisionsView all revisions

Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
Weight
209
Subjects
N/A
Pages
497 Pages
Size
H:254mm, W:
Topic
Unknown
Book Title
Neural Networks and Deep Learning: A Textbook by Charu C. Aggarw
ISBN
9783030068561
Publication Year
2019
Type
Textbook
Format
Paperback
Language
English
Publication Name
Neural Networks and Deep Learning: a Textbook
Item Height
254mm
Author
Charu C. Aggarwal
Publisher
Springer Nature Switzerland A&G
Item Width
178mm
Subject
Computer Science
Item Weight
986g
Number of Pages
497 Pages

About this product

Product Information

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. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship 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. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Product Identifiers

Publisher
Springer Nature Switzerland A&G
ISBN-13
9783030068561
eBay Product ID (ePID)
14046693431

Product Key Features

Author
Charu C. Aggarwal
Publication Name
Neural Networks and Deep Learning: a Textbook
Format
Paperback
Language
English
Subject
Computer Science
Publication Year
2019
Type
Textbook
Number of Pages
497 Pages

Dimensions

Item Height
254mm
Item Width
178mm
Item Weight
986g

Additional Product Features

Title_Author
Charu C. Aggarwal
Country/Region of Manufacture
Switzerland

Item description from the seller

Business seller information

Charlies Chapters Ltd
Charles Sword
81 Balmoral Drive
SOUTHPORT
Merseyside
PR9 8QH
United Kingdom
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:liamEku.oc.sretpahcseilrahc@skoobwontiyub
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Value added tax number:
  • GB 920245361
Company registration number:
  • 06386895
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