Neural Networks and Deep Learning, Springer, September 2018

Charu C. Aggarwal.

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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: Although 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.

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.

Chapter 1: An Introduction to Neural Networks

Latex source of slides and figures

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Latex source of slides and figures

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Latex source of slides and figures

Latex source of slides and figures

Latex source of slides and figures

Latex source of slides and figures