"Applied Linear Algebra: Core Principles" is a comprehensive guide that delves into the principles, methodologies, and practical applications of linear algebra in various fields of science, engineering, and technology. Combining theoretical foundations, computational techniques, and real-world examples, this book offers a holistic approach to understanding and utilizing linear algebra concepts.
Covering a wide range of topics, including vector spaces, matrices, eigenvalue problems, singular value decomposition, and numerical techniques, readers will gain a thorough understanding of both fundamental and advanced principles. Real-world applications in data science, machine learning, signal processing, control systems, and image processing are integrated throughout, demonstrating the practical relevance of linear algebra.

Complex mathematical concepts are presented in a clear and accessible manner, making the book suitable for students, researchers, and practitioners with varying levels of mathematical background. Detailed explanations, illustrative examples, and step-by-step solutions aid comprehension and retention. An interdisciplinary approach connects theoretical concepts with practical applications, highlighting the versatility of linear algebra in solving real-world problems.

Extensive references to literature, research papers, and online resources enable readers to explore topics in greater depth. This book is an invaluable resource for students, researchers, and professionals seeking to apply linear algebra techniques in their work across various domains.