diff --git a/README.md b/README.md index efd0875..c3dbd5a 100644 --- a/README.md +++ b/README.md @@ -8,12 +8,22 @@ https://www.coursera.org/learn/machine-learning -本项目包含课程中的所有课后作业以及笔记，目前正在将手写笔记进行电子化。 +本项目包含课程中的课后作业以及笔记： 1. 笔记(notes)都为中文，为了便于复习和扩充等，尽量会按照视频目录，以及视频内容进行提炼整理。 2. 遵循[荣誉准则][honor code]，现已移除已通过的课后编程作业的源代码，之后会改为指导笔记形式陆续更新。 + + +电子版笔记基于手写笔记，时间有限再加上为了追求清晰精确，更新较慢请大佬们谅解😭。 + +码的很辛苦，大佬们如果觉得笔记整理的还不错，记得保持关注，也欢迎分享哦。 + +感谢支持(\*^_^\*)。 + + + For Andrew Ng's machine learning course on Coursera. Including assignments and notes, notes are WIP. @@ -73,8 +83,8 @@ By: Scruel [zhihu]: https://zhuanlan.zhihu.com/p/32781741 [baidupan]: https://pan.baidu.com/s/1mkmnRIC [bilibili_zh]: http://www.bilibili.com/video/av9912938?bbid=F8173D95-FF96-47EF-B7F4-0779D698B8051978infoc -[bilibili_en1]: https://www.bilibili.com/video/av17624209/?from=search&seid=15848135050308500663 -[bilibili_en2]: https://www.bilibili.com/video/av17624412/?from=search&seid=15848135050308500663 +[bilibili_en1]: https://www.bilibili.com/video/av17624209/?from=search&seid=15848135050308500663 +[bilibili_en2]: https://www.bilibili.com/video/av17624412/?from=search&seid=15848135050308500663 [GitHub with MathJax]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima [Typora]: https://typora.io/ [honor code]: https://www.coursera.org/learn/machine-learning/supplement/nh65Z/machine-learning-honor-code diff --git a/index.html b/index.html index 794bd47..1ab2d19 100644 --- a/index.html +++ b/index.html @@ -220,7 +220,7 @@ header, .context-menu, .megamenu-content, footer { font-family: "Segoe UI", Aria -

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

 

+

吴恩达(Andrew Ng)机器学习公开课中文笔记

电子版笔记基于手写笔记，时间有限再加上为了追求清晰精确，更新较慢请大佬们谅解😭。

码的很辛苦，大佬们如果觉得笔记整理的还不错，记得保持关注，也欢迎分享哦。

感谢支持(*^_^*)。

GitHub 项目首页 | 知乎文章

 

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)

 

 

License

Creative Commons License

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

 

By: Scruel