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This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising. The design of the DL models based on artificial neural networks is influenced by the structure and operation of the brain. This book presents a comprehensive resource for those who seek a solid grasp of the techniques in DL.
Key features:
- Provides knowledge on theory and design of state-of-the-art deep learning models for real-world applications
- Explains the concepts and terminology in problem-solving with deep learning
- Explores the theoretical basis for major algorithms and approaches in deep learning
- Discusses the enhancement techniques of deep learning models
- Identifies the performance evaluation techniques for deep learning models
Accordingly, the book covers the entire process flow of deep learning by providing awareness of each of the widely used models. This book can be used as a beginners' guide where the user can understand the associated concepts and techniques. This book will be a useful resource for undergraduate and postgraduate students, engineers, and researchers, who are starting to learn the subject of deep learning.
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).
Summary
This book focuses on Deep Learning (DL), which is an important aspect of data science, that includes predictive modelling.
