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sw_machine_learning

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machine learning

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machine learning

[知识图谱](https://github.com/yunshuipiao/cheatsheets-ai-code/blob/master/md/img.md)  
[maching-learning](https://github.com/yunshuipiao/cheatsheets-ai-code/blob/master/md/machine-learning.md)

### personal blog

* [机器学习之线性回归(纯python实现)](https://github.com/yunshuipiao/SWBlog/blob/master/maching%20learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8B%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92(%E7%BA%AFpython%E5%AE%9E%E7%8E%B0).md)  
* [机器学习之逻辑回归(纯python实现)](https://github.com/yunshuipiao/SWBlog/blob/master/maching%20learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8B%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92(%E7%BA%AFpython%E5%AE%9E%E7%8E%B0).md)  
* [机器学习之贝叶斯分类(python实现)](https://github.com/yunshuipiao/SWBlog/blob/master/maching%20learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8B%E8%B4%9D%E5%8F%B6%E6%96%AF%E5%88%86%E7%B1%BB(python%E5%AE%9E%E7%8E%B0).md)  
* [机器学习之kNN算法(纯python实现)](https://github.com/yunshuipiao/SWBlog/blob/master/maching%20learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8BkNN%E7%AE%97%E6%B3%95(%E7%BA%AFpython%E5%AE%9E%E7%8E%B0).md)
* [机器学习之k-means聚类算法(python实现)](https://github.com/yunshuipiao/SWBlog/blob/master/maching_learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8Bk-means%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95(python%E5%AE%9E%E7%8E%B0).md)  
* [机器学习之决策树ID3(python实现)](https://github.com/yunshuipiao/SWBlog/blob/master/maching_learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8B%E5%86%B3%E7%AD%96%E6%A0%91ID3(python%E5%AE%9E%E7%8E%B0).md)
* [机器学习之随机森林(简单理解)](https://github.com/yunshuipiao/SWBlog/blob/master/maching_learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8B%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97(%E7%AE%80%E5%8D%95%E7%90%86%E8%A7%A3).md)
* [机器学习之SVM(简单理解)](https://github.com/yunshuipiao/SWBlog/blob/master/maching_learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8BSVM(%E7%AE%80%E5%8D%95%E7%90%86%E8%A7%A3).md)
* [机器学习之分类回归树(python实现CART)](https://github.com/yunshuipiao/SWBlog/blob/master/maching_learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8B%E5%88%86%E7%B1%BB%E5%9B%9E%E5%BD%92%E6%A0%91(python%E5%AE%9E%E7%8E%B0CART).md)
* [机器学习之GBDT(简单理解)](https://github.com/yunshuipiao/SWBlog/blob/master/maching_learning/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B9%8BGBDT(%E7%AE%80%E5%8D%95%E7%90%86%E8%A7%A3).md)
  
### 推荐阅读

* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)  
   通过python，numpy搭建简单ANN入手，讲解神经网络的结构，训练，优化，到深度学习的介绍，内容丰富。
* [colah.github.io](http://colah.github.io/)  
   CNN，RNN和神经网络可视化的高质量质量博客介绍
* [cs229:linear and logistic regression](http://cs229.stanford.edu/notes/cs229-notes1.pdf)  
  线性回归和逻辑回归的原理及公式推导过程，涉及为什么用最小均方，对数损失作为损失函数，以及sigmoid的由来，softmax regression的推导过程，特别值得一读。
* [Pattern Recognition and Machine Learning](https://book.douban.com/subject/2061116/)  
   模式识别和机器学习的必读书目
* [svm:支持向量机通俗导论](https://blog.csdn.net/v_july_v/article/details/7624837)  
  目前看到过svm最全面，并且通俗易懂的教程，从来源，问题的求解，核函数kernel本质，以及证明各方面去了解svm。

### interview

### 书籍下载

* [非常好的It电子书下载地址 http://www.allitebooks.com](http://www.allitebooks.com)

sw_machine_learning

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