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This book focuses on Deep Learning (DL), which is an important aspect of data science, that includes predictive modelling.
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This book focuses on Deep Learning (DL), which is an important aspect of data science, that includes predictive modelling.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 184
- Erscheinungstermin: 16. Oktober 2023
- Englisch
- Abmessung: 160mm x 243mm x 19mm
- Gewicht: 440g
- ISBN-13: 9781032473246
- ISBN-10: 103247324X
- Artikelnr.: 67822570
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 184
- Erscheinungstermin: 16. Oktober 2023
- Englisch
- Abmessung: 160mm x 243mm x 19mm
- Gewicht: 440g
- ISBN-13: 9781032473246
- ISBN-10: 103247324X
- Artikelnr.: 67822570
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).
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.
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.
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.
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.