Mathematical Foundations for Deep Learning bridges the gap between theoretical mathematics and practical applications in artificial intelligence (AI). This guide delves into the fundamental mathematical concepts that power modern deep learning, equipping readers with the tools and knowledge needed to excel in the rapidly evolving field of artificial intelligence.
Designed for learners at all levels, from beginners to experts, the book makes mathematical ideas accessible through clear explanations, real-world examples, and targeted exercises. Readers will master core concepts in linear algebra, calculus, and optimization techniques; understand the mechanics of deep learning models; and apply theory to practice using frameworks like TensorFlow and PyTorch.
By integrating theory with practical application, Mathematical Foundations for Deep Learning prepares you to navigate the complexities of AI confidently. 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.
Embark on an enlightening journey that fosters critical thinking and continuous learning. Invest in your future with a solid mathematical base, reinforced by case studies and applications that bring theory to life, and gain insights into the future of 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.
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
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 researchincluding 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.
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