Practical linear algebra for data science: from core concepts to applications using python
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 de...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Sebastopol, CA
O'Reilly Media, Inc.
2022
|
| Ausgabe: | First edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781098120603/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on June 12, 2023) |
| Umfang: | 1 Online-Ressource (xiii, 311 Seiten) illustrations |
| ISBN: | 1098120582 9781098120573 1098120574 9781098120580 |
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| spelling | Cohen, Mike X. 1979- VerfasserIn aut Practical linear algebra for data science from core concepts to applications using python Mike X Cohen First edition. Sebastopol, CA O'Reilly Media, Inc. 2022 ©2022 1 Online-Ressource (xiii, 311 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on June 12, 2023) 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. Algebras, Linear Data processing Python (Computer program language) Algèbre linéaire ; Informatique Python (Langage de programmation) Algebras, Linear ; Data processing 1098120612 Erscheint auch als Druck-Ausgabe 1098120612 |
| spellingShingle | Cohen, Mike X. 1979- Practical linear algebra for data science from core concepts to applications using python Algebras, Linear Data processing Python (Computer program language) Algèbre linéaire ; Informatique Python (Langage de programmation) Algebras, Linear ; Data processing |
| title | Practical linear algebra for data science from core concepts to applications using python |
| title_auth | Practical linear algebra for data science from core concepts to applications using python |
| title_exact_search | Practical linear algebra for data science from core concepts to applications using python |
| title_full | Practical linear algebra for data science from core concepts to applications using python Mike X Cohen |
| title_fullStr | Practical linear algebra for data science from core concepts to applications using python Mike X Cohen |
| title_full_unstemmed | Practical linear algebra for data science from core concepts to applications using python Mike X Cohen |
| title_short | Practical linear algebra for data science |
| title_sort | practical linear algebra for data science from core concepts to applications using python |
| title_sub | from core concepts to applications using python |
| topic | Algebras, Linear Data processing Python (Computer program language) Algèbre linéaire ; Informatique Python (Langage de programmation) Algebras, Linear ; Data processing |
| topic_facet | Algebras, Linear Data processing Python (Computer program language) Algèbre linéaire ; Informatique Python (Langage de programmation) Algebras, Linear ; Data processing |
| work_keys_str_mv | AT cohenmikex practicallinearalgebrafordatasciencefromcoreconceptstoapplicationsusingpython |