机读格式显示(MARC)

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• 017 __ |a CN |b 10-2022-479

• 020 __ |a 9787576605884 |q paperback |c CNY109.00

• 040 __ |a SLY |b eng |e rda |c SLY |d PUL

• 050 _4 |a QA185.D37 |b C6 2023

• 082 04 |a 512.5 |2 23

• 090 __ |a O151.2/E36

• 093 __ |a O151.2 |2 5

• 093 __ |a TP312PY |2 5

• 099 __ |a CAL 022023053570

• 100 1_ |a Cohen, Mike X., |d 1979- |e author.

• 245 10 |a Practical linear algebra for data science / |c Mike X Cohen = 数据科学中的实用线性代数 / [荷兰]迈克·X. 科恩著.

• 246 31 |a 数据科学中的实用线性代数

• 260 __ |a 南京 : |b 东南大学出版社, |c 2023.

• 300 __ |a xiii, 311 pages : |b illustrations ; |c 24 cm

• 336 __ |a text |b txt |2 rdacontent

• 337 __ |a unmediated |b n |2 rdamedia

• 338 __ |a volume |b nc |2 rdacarrier

• 500 __ |a Includes index.

• 520 __ |a 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. -- |c Provided by publisher.

• 534 __ |p Reprint. Originally published: |c Sebastopol, CA : O'Reilly, 2022.

• 650 _0 |a Algebras, Linear |x Data processing.

• 650 _0 |a Python (Computer program language)

• 920 __ |a 261060 |z 1

• 998 __ |a WUL