Deep Learning - A Beginners'' Guide

This book focuses on Deep Learning (DL), which is an important aspect of data science, that includes predictive modelling.

List of contents

1. Introduction. 2. Concepts and Terminology. 3. State-of-the-Art Deep Learning Models: Part I. 4. State-of-the-Art Deep Learning Models: Part II. 5. Advanced Learning Techniques. 6. Enhancement of Deep Learning Architectures. 7. Performance Evaluation Techniques.

About the author

Dulani Meedeniya is a Professor in Computer Science and Engineering at the University of Moratuwa, Sri Lanka. She holds a PhD in Computer Science from the University of St Andrews, United Kingdom. She is the director of the Bio-Health Informatics group at her department and engages in a number of collaborative research projects. She is a co-author of 100+ publications in indexed journals, peer-reviewed conferences, and book chapters. Prof. Dulani has received several awards and grants for her contribution to research. She serves as a reviewer, program committee, and editorial team member in many international conferences and journals. Her main research interests are deep learning, software modeling and design, bio-health informatics, and technology-enhanced learning. She is a Fellow of HEA (UK), MIET, Senior Member of IEEE, Member of ACM, and a Chartered Engineer registered at EC (UK).

Product details

• Authors: Dulani Meedeniya
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 27.06.2025
• EAN: 9781032487960
• ISBN: 978-1-0-3248796-0
• No. of pages: 184
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Data communication, networks; Data Mining, Artificial Intelligence, COMPUTERS / Programming / Games, COMPUTERS / Machine Theory, MATHEMATICS / Arithmetic, algorithms and data structures, Algorithms & data structures, Automatic control engineering, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Data Analytics