- 数据科学中的实用线性代数(影印版)(英文版)
- - 作者：(荷兰)迈克·X.科恩|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576605884
  - 出版日期：2023/03/01
  - 页数：311
- 售价：43.6

内容大纲
如果你想从事计算或技术领域的工作，理解线性代数是少不了的。线性代数的研究对象是矩阵及其运算，是几乎所有计算机算法和分析的数学基础。但它在几十年前的教科书中的呈现方式与专业人员如今用来解决现实世界问题的方式有很大不同。
这本来自Mike X Cohen的实用指南讲授了以Python实现的线性代数的核心概念，包括如何在数据科学、机器学习、深度学习、计算模拟和生物医学数据处理应用中使用它们。有了这本书，理解、实现和适应繁多的现代分析方法和算法将不再是问题。
作者介绍
迈克·X.科恩是荷兰唐德斯研究所（拉德堡德大学医学中心）的神经科学副教授。他在科学编程、数据分析、统计学和相关主题的教学方面拥有20多年的经验，并且已经创作了多门在线课程和教材。Mike身上有一种冷幽默感，喜欢紫色的东西。
目录
Preface
1. Introduction
  What Is Linear Algebra and Why Learn It
  About This Book
  Prerequisites
  Math
  Attitude
  Coding
  Mathematical Proofs Versus Intuition from Coding
  Code, Printed in the Book and Downloadable Online
  Code Exercises
  How to Use This Book (for Teachers and Self Learners)
2. Vectors, Part 1
  Creating and Visualizing Vectors in NumPy
  Geometry of Vectors
  Operations on Vectors
  Adding Two Vectors
  Geometry of Vector Addition and Subtraction
  Vector-Scalar Multiplication
  Scalar-Vector Addition
  Transpose
  Vector Broadcasting in Python
  Vector Magnitude and Unit Vectors
  The Vector Dot Product
  The Dot Product Is Distributive
  Geometry of the Dot Product
  Other Vector Multiplications
  Hadamard Multiplication
  Outer Product
  Cross and Triple Products
  Orthogonal Vector Decomposition
  Summary
  Code Exercises
3. Vectors, Part 2
  Vector Sets
  Linear Weighted Combination
  Linear Independence
  The Math of Linear Independence
  Independence and the Zeros Vector
  Subspace and Span
  Basis
  Definition of Basis
  Summary
  Code Exercises
4. Vector Applications
  Correlation and Cosine Similarity
  Time Series Filtering and Feature Detection
  k-Means Clustering
  Code Exercises
  Correlation Exercises

  Filtering and Feature Detection Exercises
  k-Means Exercises
5. Matrices, Part 1
  Creating and Visualizing Matrices in NumPy
  Visualizing, Indexing, and Slicing Matrices
  Special Matrices
  Matrix Math: Addition, Scalar Multiplication, Hadamard Multiplication
  Addition and Subtraction
  "Shifting" a Matrix
  Scalar and Hadamard Multiplications
  Standard Matrix Multiplication
  Rules for Matrix Multiplication Validity
  Matrix Multiplication
  Matrix-Vector Multiplication
  Matrix Operations: Transpose
……
6. Matrices, Part 2
7. Matrix Applications
8. Matrix Inverse
9. Orthogonal Matrices and QR Decomposition
10. Row Reduction and LU Decomposition
11. General Linear Models and Least Squares
12. Least Squares Applications
13. Eigendecomposition
14. Singular Value Decomposition
15. Eigendecomposition and SVD Applications
16. Python Tutorial

内容大纲

作者介绍

目录

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