一本关于深度学习进展方面的书。
Advances in Deep Learning-Springer (2020)



This book discusses the state-of-the-art deep learning models used by researchers recently. Various deep architectures and their components are discussed in detail. Algorithms that are used to train deep architectures with fast convergence rate are
illustrated with applications. Various fine-tuning algorithms are discussed for optimizing the deep models. These deep architectures not only are capable of learning complex tasks but can even outperform humans in some dedicated applications.
Despite the remarkable advances in this area, training deep architectures with a huge number of hyper-parameters is an intricate and ill-posed optimization problem. Various challenges are outlined at the end of each chapter. Another issue with
deep architectures is that learning becomes computationally intensive when large volumes of data are used for training. The book describes a transfer learning approach for faster training of deep models. The use of this approach is demonstrated in fingerprint datasets.

The book is organized into eight chapters:
Chapter 1 starts with an introduction to machine learning followed by fundamental limitations of traditional machine learning methods. It introduces deep networks and then briefly discusses why to use deep learning and how deep learning works.

Chapter 2 of the book is dedicated to one of the most successful deep learning techniques known as convolutional neural networks (CNNs). The purpose of this chapter is to give its readers an in-depth but easy and uncomplicated explanation of
various components of convolutional neural network architectures.

Chapter 3 discusses the training and learning process of deep networks. The aim of this chapter is to provide a simple and intuitive explanation of the backpropagation algorithm for a deep learning network. The training process has been
explained step by step with easy and straightforward explanations.

Chapter 4 focuses on various deep learning architectures that are based on CNN. It introduces a reader to block diagrams of these architectures. It discusses how deep learning architectures have evolved while addressing the limitations of previous deep learning networks.

Chapter 5 presents various unsupervised deep learning architectures. The basics of architectures and associated algorithms falling under the unsupervised category are outlined.

Chapter 6 discusses the application of supervised deep learning architecture for face recognition problem. A comparison of the performance of supervised deep learning architecture with traditional face recognition methods is provided in this
chapter.

Chapter 7 focuses on the application of convolutional neural networks (CNNs) for fingerprint recognition. This chapter extensively explains automatic fingerprint recognition with complete details of the CNN architecture and methods used to
optimize and enhance the performance. In addition, a comparative analysis of deep learning and non-deep learning methods is presented to show the performance difference.

Chapter 8 explains how to apply the unsupervised deep networks to handwritten digit classification problem. It explains how to build a deep learning model in two steps, where unsupervised training is performed during the first step and supervised
fine-tuning is carried out during the second step.