About this book
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.
- Book Title Neural Networks and Deep Learning
- Book Subtitle A Textbook
- DOI https://doi.org/10.1007/978-3-319-94463-0
- Copyright Information Springer International Publishing AG, part of Springer Nature 2018
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-319-94462-3
- Softcover ISBN 978-3-030-06856-1
- eBook ISBN 978-3-319-94463-0
- Edition Number 1
- Number of Pages XXIII, 497
- Number of Illustrations 128 b/w illustrations, 11 illustrations in colour
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