pThis textbook covers both classical and modern models in deep learning and includes examples and exercises throughout the chapters. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories p pppbThe basics of neural networks b The backpropagation algorithm is discussed in Chapter 2.ppMany traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linearlogistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks.pp ppbFundamentals of neural networks b A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function RBF networks and restricted Boltzmann machines.p pppbAdvanced topics in neural