Название: Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing
Автор: Gyanendra Verma, Rajesh Doriya
Издательство: Bentham Books
Год: 2023
Страниц: 270
Язык: английский
Формат: pdf (true), epub
Размер: 37.6 MB

This book is a detailed reference guide on Deep Learning and its applications. It aims to provide a basic understanding of Deep Learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by Computer Science academics and researchers. By the end of the book, the reader will become familiar with different Deep Learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries.

Machine Learning proved its usefulness in many applications in the domain of Image Processing and Computer Vision, Medical Imaging, Satellite imaging, Remote Sensing, Surveillance, etc ., over the past decade. At the same time, Machine Learning methods themselves have evolved, particularly Deep Learning methods that have demonstrated significant performance over traditional Machine Learning algorithms.

Today’s Deep Learning has become researchers’ first choice in contrast to traditional Machine Learning due to its apex performance in many applications in the domain of speech, image, and text processing. Deep Learning algorithms provide efficient solutions to problems ranging from vision and speech to text processing. The research on Deep Learning is getting enriched day by day as we witness new learning models.

This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with Deep Learning. The basic concepts of mathematics and the hardware requirements for Deep Learning implementation, and some of its popular frameworks for medical applications are also covered.

The second part is dedicated to sentiment analysis using Deep Learning and Machine Learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented.

The final part covers artificial intelligence approaches used to explain the Machine Learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised Machine Learning technique can be used for cryptocurrency portfolio management.

The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on Artificial Intelligence applications.