Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python

Cohen, Mike X

ISBN 10: 1098120612 / ISBN 13: 9781098120610

Verlag: O'Reilly Media, 2022

Beschreibung

Condition: ACCEPTABLE - Used book in acceptable condition. Cover may include stickers/heavy wear. Heavy wear on pages, heavy highlighting/writing on pages, staining, and moisture damage (rippling/warping). All orders ship via UPS Mail Innovations - MAY TAKE UP TO 10 BUSINESS DAYS from first scan to be delivered. Creasing, curling, or bending of the covers. Frayed or tears in cover. The book is warped or bent. Bestandsnummer des Verkäufers 4J2UMU001AET

Bibliografische Details

Titel: Practical Linear Algebra for Data Science: ...

Verlag: O'Reilly Media

Erscheinungsdatum: 2022

Einband: Softcover

Zustand: Acceptable

Über diesen Titel

Inhaltsangabe:

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, deep learning, 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.

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

Über die Autorin bzw. den Autor:

Mike is an associate professor of neuroscience at the Donders Institute (Radboud University Medical Centre) in the Netherlands. He has over 20 years experience teaching scientific coding, data analysis, statistics, and related topics, and has authored several online courses and textbooks. He has a suspiciously dry sense of humor and enjoys anything purple.