Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python (Paperback)
Cohen, Mike
- 出版商: O'Reilly
- 出版日期: 2022-10-11
- 定價: $2,730
- 售價: 9.5 折 $2,594
- 貴賓價: 9.0 折 $2,457
- 語言: 英文
- 頁數: 328
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1098120612
- ISBN-13: 9781098120610
-
相關分類:
Python、程式語言、線性代數 Linear-algebra、Data Science
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相關主題
商品描述
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
商品描述(中文翻譯)
如果你想在任何計算或技術領域工作,你需要了解線性代數。作為矩陣及其操作的研究,線性代數是幾乎所有在電腦中實現的算法和分析的數學基礎。但是,幾十年前的教科書中介紹的方式與專業人士如何使用線性代數來解決現實世界的現代應用有很大不同。
這本由Mike X Cohen撰寫的實用指南以Python實現的線性代數核心概念為基礎,包括它們在數據科學、機器學習、深度學習、計算模擬和生物醫學數據處理應用中的使用。通過這本書的知識,你將能夠理解、實現和適應各種現代分析方法和算法。
這本書適合使用計算機技術和算法的從業人員和學生,它向你介紹了以下內容:
- 向量和矩陣的解釋和應用
- 矩陣運算(各種乘法和變換)
- 獨立性、秩和逆
- 應用線性代數中使用的重要分解(包括LU和QR)
- 特徵分解和奇異值分解
- 應用,包括最小二乘模型擬合和主成分分析
作者簡介
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.
作者簡介(中文翻譯)
Mike是荷蘭Donders研究所(Radboud大學醫學中心)的神經科學副教授。他擁有超過20年的教授科學編碼、數據分析、統計和相關主題的經驗,並撰寫了幾本在線課程和教科書。他有一種異常幽默的幽默感,喜歡任何紫色的東西。