Neural Networks and Deep Learning

Charu C. Aggarwal works at the IBM T. J. Watson Research Center in Yorktown Heights, New York, as a Distinguished Research Staff Member (DRSM).

He has over 350 papers published in refereed conferences and publications, and he has applied for or been granted over 80 patents. He has written or edited 18 books, including data mining textbooks, machine learning (for text), recommender systems, and outlier analysis.

Neural Networks and Deep Learning covers both classical and recent deep learning models. The theory and methods of deep learning are the key focus. The theory and algorithms of neural networks are very crucial for comprehending important concepts, such as the important design concepts of neural architectures in many applications. Why do neural networks function? When are they superior than off-the-shelf machine-learning models? When does depth come in handy? Why is it so difficult to train neural networks? What are the dangers? The book also discusses several applications to provide the practitioner a sense of how neural networks are created for different types of issues. Applications in several fields are discussed, including recommender systems, machine translation, picture captioning, image classification, reinforcement-learning based games, and text analytics.

This book's chapters are divided into three categories:

• The fundamentals of neural networks: Many traditional machine learning methods are special examples of neural networks. The first two chapters focus on understanding the link between traditional machine learning and neural networks. Special examples of neural networks include support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems. These methods are being investigated alongside newer feature engineering methods such as word2vec.
• Fundamentals of neural networks: Chapters 3 and 4 provide a full overview of training and regularization. Radial-basis function (RBF) networks and limited Boltzmann machines are discussed in Chapters 5 and 6.
• Advanced neural network topics: Chapters 7 and 8 cover recurrent neural networks and convolutional neural networks, respectively. In Chapters 9 and 10, advanced subjects such as deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced.

Graduate students, researchers, and practitioners will find the book useful. A solution manual and numerous exercises are available to aid in classroom teaching. Wherever possible, an application-centric perspective is highlighted to provide an understanding of the actual applications of each class of techniques.

Author: Charu C. Aggarwal

Link to buy: https://www.amazon.com/Neural-Networks-Deep-Learning-Textbook/dp/3030068560

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