Neural Networks and Deep Learning - A Textbook - Grand Format

This book covers both classical and modern models in deep looming. 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 wordavec. Fundamentals of neural networks : A detailed discussion of training and regularization is provided in Chapters 3 and q. Chapters 5 and 6 present radial-basis function (RIM 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 luring machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and in. 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-conicviewis highlighted in order to provide an understanding of the practical uses of each dass of techniques.