[2020 FREE EBOOK]Advances in Deep Learning
M. Arif Wani, Farooq Ahmad Bhat, Saduf Afzal, Asif Iqbal Khan
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.
#evba #etipfree #eama #kingexcelrecently. 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.
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