Mathematical Foundations for Deep Learning
eBook - ePub

Mathematical Foundations for Deep Learning

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Mathematical Foundations for Deep Learning

About this book

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.

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Yes, you can access Mathematical Foundations for Deep Learning by Mehdi Ghayoumi in PDF and/or ePUB format, as well as other popular books in Computer Science & Business Intelligence. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Table of Contents
  6. Preface
  7. Acknowledgments
  8. About the Author
  9. 1 Introduction
  10. 2 Linear Algebra
  11. 3 Multivariate Calculus
  12. 4 Probability Theory and Statistics
  13. 5 Optimization Theory
  14. 6 Information Theory
  15. 7 Graph Theory
  16. 8 Differential Geometry
  17. 9 Topology in Deep Learning
  18. 10 Harmonic Analysis for CNNs
  19. 11 Dynamical Systems and Differential Equations for RNNs
  20. 12 Quantum Computing
  21. Bibliography
  22. Index