Linear Algebra: Theory, Intuition, Code

Are you ready to dive into the vibrant world of linear algebra and see how it powers real-world applications? Welcome to this comprehensive guide, where traditional theory meets modern computational practices.
Linear algebra is the magic behind many computational sciences -- machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and more. But here's a secret: the way it's taught in traditional textbooks isn't how professionals use it in the field.
For instance, have you ever wondered about the practical importance of a matrix's "determinant"? You might be in for a surprise! This book bridges the gap between theoretical understanding and practical application, showing you not only the 'what' but also the 'how' of implementing linear algebra in real-world scenarios.
What makes this book a must-have resource? Crystal clear explanations of linear algebra concepts and theories. Multiple angles to explain ideas, a proven technique to help cement your understanding. Vivid graphical visualizations to enhance your geometric intuition of linear algebra. Real-world implementations in MATLAB and Python. After all, in today's world, you seldom solve math problems by hand. Software is the way forward! A range of topics from beginner to intermediate levels, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition. Emphasis on the application-oriented aspects of linear algebra and matrix analysis. Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition. Ready-to-use codes in MATLAB and Python to bring linear algebra concepts to life on your computer. All codes can be downloaded from https://github.com/mikexcohen/LinAlgBook. A unique blend of hand-solved exercises and advanced code challenges. Remember, math is not a spectator sport!
Whether you're just starting your journey in linear algebra or seeking to apply these concepts to data analyses on computers (such as statistics or signal processing), this book is your go-to guide. With this book at your side, you won't just learn linear algebra; you'll experience it!