2021-12-15 22:30:24

docs/linalg/SUMMARY.md

@@ -9,20 +9,20 @@
 +   [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)
-+   [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)
++   [机器学习中的特征分解，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)
-+   [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)
++   [机器学习中的 NumPy 张量的温和介绍](introduction-to-tensors-for-machine-learning.md)
++   [机器学习中的线性代数中的矩阵类型简介](introduction-to-types-of-matrices-in-linear-algebra.md)
++   [机器学习中的线性代数备忘单](linear-algebra-cheat-sheet-for-machine-learning.md)
++   [深度学习中的线性代数](linear-algebra-for-deep-learning.md)
++   [机器学习中的线性代数（7 天迷你课程）](linear-algebra-machine-learning-7-day-mini-course.md)
++   [机器学习中的线性代数](linear-algebra-machine-learning.md)
++   [机器学习中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)
-+   [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)

docs/lstm/SUMMARY.md

@@ -2,7 +2,7 @@
 +   [Keras中长短期记忆模型的5步生命周期](5-step-life-cycle-long-short-term-memory-models-keras.md)
 +   [长短期记忆循环神经网络的注意事项](attention-long-short-term-memory-recurrent-neural-networks.md)
 +   [CNN长短期记忆网络](cnn-long-short-term-memory-networks.md)
-+   [面向深度学习的循环神经网络的速成课程](crash-course-recurrent-neural-networks-deep-learning.md)
++   [深度学习中的循环神经网络的速成课程](crash-course-recurrent-neural-networks-deep-learning.md)
 +   [可变长度输入序列的数据准备](data-preparation-variable-length-input-sequences-sequence-prediction.md)
 +   [如何用Python和Keras开发用于序列分类的双向LSTM](develop-bidirectional-lstm-sequence-classification-python-keras.md)
 +   [如何在 Keras 中开发用于序列到序列预测的编解码器模型](develop-encoder-decoder-model-sequence-sequence-prediction-keras.md)