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Neural Networks and Deep Learning: A Textbook by [Aggarwal, Charu C.]
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Neural Networks and Deep Learning: A Textbook Kindle Edition


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Length: 524 pages Language: English
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Product description

Review

“The book recommends itself as a stepping-stone of the research-intensive area of deep learning and a worthy continuation of the previous textbooks written by the author … . Thanks to its systematic and thorough approach complemented with the variety of resources (bibliographic and software references, exercises) neatly presented after each chapter, it is suitable for audiences of varied expertise or background.” (Irina Ioana Mohorianu, zbMATH 1402.68001, 2019)

Product Description

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 details

  • Format: Kindle Edition
  • File Size: 29883 KB
  • Print Length: 497 pages
  • Publisher: Springer; 1st ed. 2018 edition (25 August 2018)
  • Sold by: Amazon Australia Services, Inc.
  • Language: English
  • ASIN: B07FKF5HY7
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #428,614 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most helpful customer reviews on Amazon.com

Amazon.com: 4.1 out of 5 stars 20 reviews
AKS
5.0 out of 5 starsThe best book in the academic genre
5 October 2018 - Published on Amazon.com
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17 people found this helpful
Bhanu Ganesh
5.0 out of 5 starsDefinitely a Must Have - If you are interested in Neural Networks and Deep Learning
2 November 2018 - Published on Amazon.com
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14 people found this helpful
Amazon Customer
5.0 out of 5 starsCan't recommend it enough
13 December 2018 - Published on Amazon.com
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13 people found this helpful
serge
5.0 out of 5 starsGreat blend of theory, math, and how to implement deep neural networks
23 December 2018 - Published on Amazon.com
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11 people found this helpful
Stergios Papadimitriou
5.0 out of 5 starsGreat book!
16 November 2018 - Published on Amazon.com
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10 people found this helpful