Neural networks and deep learning : a textbook /

Neural networks and deep learning : a textbook /

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Bibliographic Details
Author / Creator:	Aggarwal, Charu C., author.
Imprint:	Cham, Switzerland : Springer, 2018.
Description:	1 online resource (xxiii, 497 pages) : illustrations (some color)
Language:	English
Subject:	Neural networks (Computer science) Machine learning. Machine learning. Neural networks (Computer science)
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11690581

Hidden Bibliographic Details
ISBN:	9783319944630 3319944630 9783319944647 3319944649 9783319944623 3319944622
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references and index. Online resource; title from PDF title page (SpringerLink, viewed August 30, 2018).
Summary:	This book covers both classical and modern models in deep learning. 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.
Other form:	Print version: Aggarwal, Charu C. Neural networks and deep learning. Cham, Switzerland : Springer, 2018 3319944622 9783319944623
Standard no.:	10.1007/978-3-319-94463-0 10.1007/978-3-319-94

Table of Contents:

1 An Introduction to Neural Networks
2 Machine Learning with Shallow Neural Networks
3 Training Deep Neural Networks
4 Teaching Deep Learners to Generalize
5 Radical Basis Function Networks
6 Restricted Boltzmann Machines
7 Recurrent Neural Networks
8 Convolutional Neural Networks
9 Deep Reinforcement Learning
10 Advanced Topics in Deep Learning.