数据科学中的实用线性代数(影印版)

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。

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