Advances in Deep Learning, Volume 2
Description
This book describes novel ways of using deep learning to solve real-world problems. It covers advanced deep learning topics like neural architecture search, ensemble deep learning, transfer learning techniques, lightweight architectures, hybrid deep learning approaches, and generative adversarial networks. The book discusses the use of these advanced topics in selected applications like image classification, object detection, image steganography, protein secondary structure prediction, and gene expression data classification. Various challenges and future research directions falling under the scope of these topics are discussed.
More details
Other editions
Additional editions
Persons
Dr. M. Arif Wani received an M. Tech. degree in Computer Technology from the Indian Institute of Technology, Delhi, and a Ph.D. degree in Computer Vision from Cardiff University, UK. Currently, he is a Professor at the University of Kashmir, having previously served as a Professor at California State University, Bakersfield. His research interests are in the area of machine learning, with a focus on neural networks, deep learning, inductive learning, support vector machines, computer vision, pattern recognition, and classification tasks. He has published many papers in reputed journals and conferences in these areas. Dr. Wani has co-authored the book 'Advances in Deep Learning', and co-edited many books on Machine Learning and Deep Learning applications. He is a member of many academic and professional bodies.
Sarwat Ali has a Bachelor's and Master's degree in Computer Applications from the University of Kashmir, Hazratbal, Srinagar. She has worked as a Research Associate in the Artificial Intelligence Research Center project supported by Rashtriya Uchchatar Shiksha Abhiyan (RUSA 2.0), an initiative of the Government of India. Currently, she is dedicated to her Ph.D. studies in Neural Architecture Search (NAS) at the Post Graduate Department of Computer Science (PGDCS), University of Kashmir. She has contributed to publications in prestigious journals and research conferences in this specialized field. Sarwat's academic journey has been marked by achievements, including the receipt of gold medals for both her Bachelor's and Master's degrees from the University of Kashmir. She has also achieved success in the (University Grants Commission National Eligibility Test) UGC NET examination.
Mukhtar Ahmad Sofi received MCA and M.Tech. in Computer Science and engineering degrees from Pondicherry Central University and a Ph.D. in Computer Science from the University of Kashmir. Currently, he is an Assistant Professor in the Information Technology Department at the BVRIT Hyderabad College of Engineering for Women. Dr. Sofi's diverse research interests span data mining, machine learning, deep learning, natural language processing, computational biology, and bioinformatics. He has made notable contributions to these areas through numerous research articles published in prestigious academic journals and presentations at internationally acclaimed conferences. Dr. Sofi continues to make impactful contributions to the academic and research community.
Bisma Sultan earned a B.Tech. in Computer Science and Engineering from the University of Kashmir, an M.Tech. in Computer Science from the University of Jammu, and completed her Ph.D. in Computer Science at the University of Kashmir. She qualified National Eligibility Test (NET) and the Graduate Aptitude Test in Engineering (GATE). She served as a Senior Research Fellow in the Department of Computer Sciences at the University of Kashmir. Currently, she holds a faculty position within the Department of Computer Sciences at the University of Kashmir. Dr. Bisma's research interests encompass a broad spectrum of fields, including machine learning, deep learning, information security, web technologies, and networking. She has made contributions to these areas by publishing a number of research articles in high-ranking academic journals and conferences.
Content
Introduction to Deep Learning Architectures.- Evolutionary Algorithm-based Neural Architecture SearchEvolutionary Algorithm-based Neural Architecture Search.- Gradient-based Neural Architecture Search.