Neural Networks and Deep Learning, Springer, September 2018
Charu C. Aggarwal.
Book on neural networks and deep learning
Table of Contents
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This is an comprehensive textbook on neural networks and deep learning.
The following aspects are covered:
The basics of neural networks: Chapters 1 and 2
discuss the basics of neural network design and also the
fundamentals of training them. The simulation of various machine
learning models with neural networks is provided. Examples include least-squares regression,
SVMs, logistic regression, Widrow-Hoff learning, singular value decomposition,
and recommender systems. Recent models like word2vec are also explored, together
with their connections with traditional matrix factorization. Exploring the interface
between machine learning and neural networks is important because it
provides a deeper understanding of how neural networks generalize known machine
learning methods, and the cases in which neural networks
have advantages over traditional machine learning.
Challenges in training neural networks:
Chapters 1 and 2 provide an overview of the training methods for
neural networks, a more detailed understanding of the training
challenges is provided in Chapters 3 and 4. In particular, issues
related to network depth and also overfitting are discussed. Chapter 5 presents
a classical architecture, referred to as radial-basis function networks,
which is no longer used frequently.
Advanced architectures and applications: A lot of the
success in neural network design is a result of the specialized
architectures for various domains and applications. Examples of
such specialized architectures include recurrent neural networks and
convolutional neural networks. Since the specialized architectures
form the key to the understanding of neural network performance in
various domains, most of the book will be devoted to this setting.
Several advanced topics like deep reinforcement learning, neural Turing mechanisms, and generative
adversarial networks are discussed.
Some of the ``forgotten'' architectures like RBF networks and
Kohonen self-organizing maps are included because of their potential in many applications.
The book is written for graduate students, researchers, and
practitioners. The book does require knowledge of probability and
linear algebra. Furthermore basic knowledge of machine learning is
helpful. 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 give the reader
a feel for the technology.
Cost-effective methods for obtaining electronic and hardcopy versions
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rapidly increasing number of universities in Asia are also
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About the Author
Charu Aggarwal is a Distinguished Research Staff Member (DRSM) at the
IBM T. J. Watson Research
Center in Yorktown Heights, New York. He completed his B.Tech. from IIT
Kanpur in 1993 and his Ph.D. from
Massachusetts Institute of Technology in 1996. He has worked
extensively in the field of data mining, with particular interests in
data streams, privacy, uncertain data and social network analysis.
He has published 17 (6 authored and 11 edited) books, over 350 papers in
refereed venues, and has applied for or been granted over 80 patents.
His h-index is 80.
Because of the commercial value of the above-mentioned patents,
he has received several invention achievement awards and has thrice been
designated a Master Inventor at IBM.
He is a recipient of an IBM Corporate
Award (2003) for his work on bio-terrorist threat detection in data
streams, a recipient of the IBM Outstanding Innovation Award (2008)
for his scientific contributions to privacy technology, and two IBM Outstanding Technical Achievement
Awards for his work on streaming systems and high-dimensional data analysis.
He has received two best paper awards and an EDBT
Test-of-Time Award (2014). He has received the IEEE ICDM Research Contributions
Award (2015), which is one of two highest awards for research in the field of data mining.
He has served as the general or program co-chair of the IEEE Big Data
Conference (2014), the ICDM Conference (2015), the ACM CIKM Conference
(2015), and the KDD Conference (2016). He also co-chaired the data
mining track at the WWW Conference 2009. He served as an associate
editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the ACM Transactions on Knowledge Discovery and Data Mining Journal , an action editor of the Data Mining and Knowledge Discovery Journal ,
an associate editor of the IEEE Transactions on Big Data, and an
associate editor of the Knowledge and Information Systems Journal. He is and editor-in-chief
of the ACM Transactions on Knowledge Discovery from Data, and is also an
editor-in-chief of the ACM SIGKDD Explorations.
He is a fellow of the SIAM (2015), ACM (2013) and the IEEE (2010) for
"contributions to knowledge discovery and data mining techniques."
Solution Manual for Book
The solution manual for the book is available here from Springer. There is a link for the solution manual on this page. If you are an instructor, then you can obtain
a copy. Please do not ask me
directly for a copy of the solution manual. It can only be distributed by Springer.