Neural Networks and Deep Learning

A Textbook

  • Charu C. Aggarwal

Table of contents

  1. Front Matter
    Pages I-XXIII
  2. Charu C. Aggarwal
    Pages 1-52
  3. Charu C. Aggarwal
    Pages 53-104
  4. Charu C. Aggarwal
    Pages 105-167
  5. Charu C. Aggarwal
    Pages 169-216
  6. Charu C. Aggarwal
    Pages 217-233
  7. Charu C. Aggarwal
    Pages 235-270
  8. Charu C. Aggarwal
    Pages 271-313
  9. Charu C. Aggarwal
    Pages 315-371
  10. Charu C. Aggarwal
    Pages 373-417
  11. Charu C. Aggarwal
    Pages 419-458
  12. Back Matter
    Pages 459-497

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.


Deep Learning Machine Learning Radial Basis Function Networks Restricted Boltzmann Machines Recurrent Neural Networks Convolutional Neural Networks Neural networks perceptron deep reinforcement learning word2vec autoencoder logistic regression dropout pretraining backpropagation conjugate gradient-descent Adam RMSProp Kohonean self-organizaing map generative adversarial networks

Authors and affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T. J. Watson Research CenterInternational Business MachinesYorktown HeightsUSA

Bibliographic information

Industry Sectors
Chemical Manufacturing
IT & Software
Consumer Packaged Goods
Materials & Steel
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences