Mathematical Foundations for Deep Learning

This book bridges the gap between theoretical mathematics and practical applications in AI. Whether you're aiming to develop practical skills for AI projects, advance to emerging trends in deep learning, or lay a strong foundation for future studies, this book serves as an indispensable resource for achieving proficiency in the field.

Sommario

Preface About the author Acknowledgements 1. Introduction 2. Linear Algebra 3. Multivariate Calculus 4. Probability Theory and Statistics 5. Optimization Theory 6. Information Theory 7. Graph Theory 8. Differential Geometry 9. Topology in Deep Learning 10. Harmonic Analysis for CNNs 11. Dynamical Systems and Differential Equations for RNNs 12. Quantum Computing

Info autore

Dr. Mehdi Ghayoumi is an Assistant Professor at the Center for Criminal Justice, Intelligence, and Cybersecurity at SUNY Canton, recognized for his excellence in teaching and research-including previous roles at SUNY Binghamton and Kent State University, where he received consecutive Teaching Awards in 2016 and 2017. His multidisciplinary research focuses on machine learning, robotics, human-robot interaction, and privacy, aiming to develop practical systems for real-world applications in manufacturing, biometrics, and healthcare. Actively contributing to the academic community, Dr. Ghayoumi develops courses in emerging technologies and serves on technical program committees and editorial boards for leading conferences and journals in his field.

Dettagli sul prodotto

• Autori: Mehdi Ghayoumi, Mehdi (University of San Diego) Ghayoumi
• Editore: Taylor & Francis Ltd.
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 05.08.2025
• EAN: 9781032690735
• ISBN: 978-1-0-3269073-5
• Pagine: 374
• Categorie: Guide e manuali; Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica; COMPUTERS / Computer Science, Applied mathematics, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Neural Networks, Neural networks and fuzzy systems, Neural networks & fuzzy systems