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If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications.
This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, Machine Learning (ML), Deep Learning (DL), computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms.
The purpose of this book is to teach you modern linear algebra. But this is not about memorizing some key equations and slugging through abstract proofs the purpose is to teach you how to think about matrices, vectors, and operations acting upon them. You will develop a geometric intuition for why linear algebra is the way it is. And you will understand how to implement linear algebra concepts in Python code, with a focus on applications in machine learning and data science.
Ideal for practitioners and students using computer technology and algorithms, this book introduces you to:
The interpretations and applications of vectors and matrices
Matrix arithmetic (various multiplications and transformations)
Independence, rank, and inverses
Important decompositions used in applied linear algebra (including LU and QR)
Eigendecomposition and singular value decomposition
Applications including least-squares model fitting and principal components analysis