Neural Networks and Deep Learning

A Textbook

by Charu C. Aggarwal

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Book Description

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.

This open book is licensed under a Creative Commons License (CC BY). You can download Neural Networks and Deep Learning ebook for free in PDF format (7.3 MB).

Book Details

Subject
Computer Science
Publisher
Springer
Published
2018
Pages
512
Edition
1
Language
English
ISBN13
9783319944623
ISBN10
3319944622
ISBN13 Digital
9783319944630
ISBN10 Digital
3319944630
PDF Size
7.3 MB
License
CC BY

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