Neural Networks and Deep Learning

Beschreibung

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 book covers the theory and algorithms of deep learning and it provides detailed discussions of the relationships of neural networks with traditional machine learning algorithms.

The mathematical aspects are concretely presented without losing accessibility.

The book is written in a textbook style, and it includes exercises, a solution manual, and instructor slides. The depth and breadth of coverage are unique to the book.

Includes supplementary material: sn.pub/extras

Request lecturer material: sn.pub/lecturer-material

Autor*in

Charu C. Aggarwal

Themen in »Neural Networks and Deep Learning«

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

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“The book recommends itself as a stepping-stone of the research-intensive area of deep learning and a worthy continuation of the previous textbooks written by the author … . Thanks to its systematic and thorough approach complemented with the variety of resources (bibliographic and software references, exercises) neatly presented after each chapter, it is suitable for audiences of varied expertise or background.” (Irina Ioana Mohorianu, zbMATH 1402.68001, 2019)
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