2021-12-15 22:02:59

docs/linalg/SUMMARY.md

 +   [Machine Learning Mastery 线性代数教程](README.md)
-+   [机器学习数学符号的基础知识](basics-mathematical-notation-machine-learning.md)
-+   [用 NumPy 数组轻松介绍广播](broadcasting-with-numpy-arrays.md)
-+   [如何从 Python 中的 Scratch 计算主成分分析（PCA）](calculate-principal-component-analysis-scratch-python.md)
-+   [用于编码器审查的计算线性代数](computational-linear-algebra-coders-review.md)
-+   [10 机器学习中的线性代数示例](examples-of-linear-algebra-in-machine-learning.md)
++   [机器学习中数学符号的基础知识](basics-mathematical-notation-machine-learning.md)
++   [NumPy 数组广播的温和介绍](broadcasting-with-numpy-arrays.md)
++   [如何在 Python 中从零开始计算主成分分析（PCA）](calculate-principal-component-analysis-scratch-python.md)
++   [面向程序员的计算线性代数回顾](computational-linear-algebra-coders-review.md)
++   [10 个机器学习中的线性代数示例](examples-of-linear-algebra-in-machine-learning.md)
 +   [线性代数的温和介绍](gentle-introduction-linear-algebra.md)
-+   [用 NumPy 轻松介绍 Python 中的 N 维数组](gentle-introduction-n-dimensional-arrays-python-numpy.md)
++   [Python NumPy 的 N 维数组的温和介绍](gentle-introduction-n-dimensional-arrays-python-numpy.md)
 +   [机器学习向量的温和介绍](gentle-introduction-vectors-machine-learning.md)
 +   [如何在 Python 中为机器学习索引，切片和重塑 NumPy 数组](index-slice-reshape-numpy-arrays-machine-learning-python.md)
 +   [机器学习的矩阵和矩阵算法简介](introduction-matrices-machine-learning.md)
-+   [温和地介绍机器学习的特征分解，特征值和特征向量](introduction-to-eigendecomposition-eigenvalues-and-eigenvectors.md)
-+   [NumPy 对期望值，方差和协方差的简要介绍](introduction-to-expected-value-variance-and-covariance.md)
++   [面向机器学习的特征分解，特征值和特征向量的温和介绍](introduction-to-eigendecomposition-eigenvalues-and-eigenvectors.md)
++   [NumPy 期望值，方差和协方差的简要介绍](introduction-to-expected-value-variance-and-covariance.md)
 +   [机器学习矩阵分解的温和介绍](introduction-to-matrix-decompositions-for-machine-learning.md)
-+   [用 NumPy 轻松介绍机器学习的张量](introduction-to-tensors-for-machine-learning.md)
-+   [用于机器学习的线性代数中的矩阵类型简介](introduction-to-types-of-matrices-in-linear-algebra.md)
-+   [用于机器学习的线性代数备忘单](linear-algebra-cheat-sheet-for-machine-learning.md)
-+   [线性代数的深度学习](linear-algebra-for-deep-learning.md)
-+   [用于机器学习的线性代数（7 天迷你课程）](linear-algebra-machine-learning-7-day-mini-course.md)
-+   [机器学习的线性代数](linear-algebra-machine-learning.md)
-+   [机器学习矩阵运算的温和介绍](matrix-operations-for-machine-learning.md)
-+   [线性代数评论没有废话指南](no-bullshit-guide-to-linear-algebra-review.md)
-+   [学习机器学习线性代数的主要资源](resources-for-linear-algebra-in-machine-learning.md)
++   [面向机器学习的 NumPy 张量的温和介绍](introduction-to-tensors-for-machine-learning.md)
++   [面向机器学习的线性代数中的矩阵类型简介](introduction-to-types-of-matrices-in-linear-algebra.md)
++   [面向机器学习的线性代数备忘单](linear-algebra-cheat-sheet-for-machine-learning.md)
++   [面向深度学习的线性代数](linear-algebra-for-deep-learning.md)
++   [面向机器学习的线性代数（7 天迷你课程）](linear-algebra-machine-learning-7-day-mini-course.md)
++   [面向机器学习的线性代数](linear-algebra-machine-learning.md)
++   [面向机器学习的矩阵运算的温和介绍](matrix-operations-for-machine-learning.md)
++   [线性代数回顾的没有废话的指南](no-bullshit-guide-to-linear-algebra-review.md)
++   [在机器学习中学习线性代数的主要资源](resources-for-linear-algebra-in-machine-learning.md)
 +   [浅谈机器学习的奇异值分解](singular-value-decomposition-for-machine-learning.md)
 +   [如何用线性代数求解线性回归](solve-linear-regression-using-linear-algebra.md)
-+   [用于机器学习的稀疏矩阵的温和介绍](sparse-matrices-for-machine-learning.md)
-+   [机器学习中向量规范的温和介绍](vector-norms-machine-learning.md)
-+   [学习线性代数用于机器学习的 5 个理由](why-learn-linear-algebra-for-machine-learning.md)
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++   [面向机器学习的稀疏矩阵的温和介绍](sparse-matrices-for-machine-learning.md)
++   [机器学习中向量范数的温和介绍](vector-norms-machine-learning.md)
++   [为机器学习学习线性代数的 5 个理由](why-learn-linear-algebra-for-machine-learning.md)