From b4db50e2841b271ca569b2b103e15b254bac9af8 Mon Sep 17 00:00:00 2001 From: scruel Date: Thu, 18 Jan 2018 01:17:13 +0800 Subject: [PATCH] update README --- README.md | 9 ++++++--- index.html | 4 ++-- week4.md | 2 ++ 3 files changed, 10 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 020aed0..c849198 100644 --- a/README.md +++ b/README.md @@ -55,14 +55,17 @@ You can read it by Typora or any other similar markdown editor. 注：由于手写笔记成稿时间较早，有所参考无法一一致谢，在此统一表示谢意！ ## License +[![Creative Commons License](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)][3] -[本(ke)人(chi)的知乎笔记文案](https://zhuanlan.zhihu.com/p/32781741) +This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][3]. -[![Creative Commons License](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)][3] +[知乎文章](https://zhuanlan.zhihu.com/p/32781741) -This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][3]. + + +By: Scruel [1]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima [2]: https://typora.io/ diff --git a/index.html b/index.html index a8ed214..aa21b0a 100644 --- a/index.html +++ b/index.html @@ -2,7 +2,7 @@ -吴恩达(Andrew Ng)机器学习公开课中文笔记 -

吴恩达(Andrew Ng)机器学习公开课中文笔记

week1

  1. 引言(Introduction)
  2. 单变量线性回归(Linear Regression with One Variable)

week2

  1. 线性代数回顾(Linear Algebra Review)
  2. 多变量线性回归(Linear Regression with Multiple Variables)
  3. Octave/Matlab 指南(Octave/Matlab Tutorial)

week3

  1. 逻辑回归(Logistic Regression)
  2. 正则化(Regularization)

week4

  1. 神经网络：表达(Neural Networks: Representation)

week5

  1. 神经网络：学习(Neural Networks: Learning)

week6

  1. 机器学习应用的建议(Advice for Applying Machine Learning)
  2. 机器学习系统设计(Machine Learning System Design)

week7

  1. 支持向量机(Support Vector Machines)

week8

  1. 无监督学习(Unsupervised Learning)
  2. 降维(Dimensionality Reduction)

week9

  1. 异常检测(Anomaly Detection)
  2. 推荐系统(Recommender Systems)

week10

  1. 大规模机器学习(Large Scale Machine Learning)

week11

  1. 实战：图像光学识别(Application Example: Photo OCR)

+

吴恩达(Andrew Ng)机器学习公开课中文笔记

GitHub 项目首页

 

week1

  1. 引言(Introduction)
  2. 单变量线性回归(Linear Regression with One Variable)

week2

  1. 线性代数回顾(Linear Algebra Review)
  2. 多变量线性回归(Linear Regression with Multiple Variables)
  3. Octave/Matlab 指南(Octave/Matlab Tutorial)

week3

  1. 逻辑回归(Logistic Regression)
  2. 正则化(Regularization)

week4

  1. 神经网络：表达(Neural Networks: Representation)

week5

  1. 神经网络：学习(Neural Networks: Learning)

week6

  1. 机器学习应用的建议(Advice for Applying Machine Learning)
  2. 机器学习系统设计(Machine Learning System Design)

week7

  1. 支持向量机(Support Vector Machines)

week8

  1. 无监督学习(Unsupervised Learning)
  2. 降维(Dimensionality Reduction)

week9

  1. 异常检测(Anomaly Detection)
  2. 推荐系统(Recommender Systems)

week10

  1. 大规模机器学习(Large Scale Machine Learning)

week11

  1. 实战：图像光学识别(Application Example: Photo OCR)

 

 

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

知乎文章

By: Scruel

 

diff --git a/week4.md b/week4.md index 3fd22a1..5cc302c 100644 --- a/week4.md +++ b/week4.md @@ -61,6 +61,8 @@ BrainPort 系统：帮助失明人士通过摄像头以及舌尖感官“看” >$\Theta^{(j)}$: 从第 $j$ 层映射到第 $j+1$ 层时的权重矩阵。 > >$\Theta^{(j)}_{v,u}$: 从第 $j$ 层的第 $u$ 个单元映射到第 $j+1$ 层的第 $v$ 个单元的权重 +> +>$s_j$: 第 $j$ 层的激活单元数目（不包含偏置单元） 注意：