The book has been divided into seven chapters. Chapter 1 elaborately deals with the fundamentals of deep learning, to enable any reader to understand the deep learning architectures elaborated in subsequent chapters. Chapter 2 deals with Convolutional Neural Networks (CNNs), which have proven to be very effective in the area of computer vision. Chapter 3 deals with Recurrent Neural Networks (RNNs) and its variants. The various types of autoencoders, which are a type of Artificial Neural Network used to learn efficient data encoding, are presented in Chapter 4. To learn the probability distribution over the set of inputs, Restricted Boltzmann Machine (RBM) is discussed in Chapter 5. Chapter 6 presents popular open source frameworks in Python for deep learning applications. Chapter 7 describes how to utilize the knowledge that you have gained from previous chapters in real-time applications.

[center][/center