分享 - 27 个机器学习、数学、Python 速查表
2017-09-16 17:38
866 查看
机器学习涉及到的方面非常多。当我开始准备复习这些内容的时候,我找到了许多不同的”速查表”, 这些速查表针对某一主题都罗列出了所有我需要知道的知识重点。最终我编译了超过 20 份机器学习相关的速查表,其中一些是我经常用到的而且我相信其他人也会从中受益。本文整理了我在网络上找到的 27 个速查表,我认为比较好。如果我有遗漏,欢迎补充。
如今机器学习领域的发展相当迅速,我可以想象出来这些资源将会很快过时,但是至少在当前,在2017年6月1日,他们都是相当流行的。
如果你们像我一样想要一次性批量下载所有资源,我我已经将 27 个速查表整理打包好了:https://pan.baidu.com/s/1mi0viGS
如果你喜欢本文,记得给我在下面点个 zan 哦。
机器学习
这里我从一些和机器学习算法相关的流程图和表格中选择了我认为最全面的几个并在下面罗列出来。Neural Network Architectures
链接: http://www.asimovinstitute.org/neural-network-zoo/![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/6de642659509f27bbad51c5fd692179b.jpg)
The Neural Network Zoo
Microsoft Azure Algorithm Flowchart
链接: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/746445b46ed2b12e702a2963b8f26634.jpg)
Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio
SAS Algorithm Flowchart
链接: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/b3c3d066e049c4b8658bc104261056ac.jpg)
SAS: Which machine learning algorithm should I use?
Algorithm Summary
链接: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/445c2491ccd12163d0684ca43ea3ae0b.jpg)
A Tour of Machine Learning Algorithms
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/7e93a8bc7615c6064a307ac7bd8337ad.jpg)
Which are the best known machine learning algorithms?
Algorithm Pro/Con
链接: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/81728a585b55d0af41bd6076ea879162.jpg)
Python
网上在线的Python资源可以说是相当的多。在这一部分,我挑选了我遇到的几个最好的速查表呈献给大家。
ML算法
链接: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/b51748f5cdb75181d3d49000536c919a.jpg)
Python基础
链接: http://datasciencefree.com/python.pdf![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/2232074072f85e13c88f8626633ee680.jpg)
链接: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/c52af50212015c98498187988cd577e5.jpg)
Numpy
链接: https://www.dataquest.io/blog/numpy-cheat-sheet/![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/be06d77e03577cbcfd32845ff4093209.jpg)
链接: http://datasciencefree.com/numpy.pdf
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/3a1152eef1809ca7619ee465d7af1951.jpg)
链接: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/e8e0d40d3992da882135cff4f50c1c19.jpg)
链接: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/fa3ee553f341de22da65cd9ad90c3dac.jpg)
Pandas
链接: http://datasciencefree.com/pandas.pdf![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/522b922a579075025c3776dff47593e9.jpg)
链接: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/648d06d9d505198e53bd9f0842ffe40e.jpg)
链接: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/4c978ad2488e4e26eab9e2cd985c695f.jpg)
Matplotlib
链接: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/e29686995089198f72c2d74f98a2cd0f.jpg)
链接: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/03c99cd5e01efa75f6657d93eedb89c7.jpg)
Scikit Learn
链接: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/de2b6c59de4822af4b721944d8bda348.jpg)
链接: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/cccda21c52bd48ed2beceb6f41c41c64.jpg)
链接: https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/1df26a499ccd15e08ed2292a427d2948.jpg)
Tensorflow
链接: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/9e2c619b206eb38c85d99405c8aa4aeb.jpg)
Pytorch
链接: https://github.com/bfortuner/pytorch-cheatsheet![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/f91f4ae4f21a4ae6803f51a9ea15ad48.jpg)
数学
如果你想真正的理解机器学习,你需要有扎实的统计学(尤其是概率论), 线性代数以及微积分基础。我在上大学的时候辅修了数学专业,但是我肯定还是需要对这些数学知识进行复习。如果你想理解常用机器学习算法背后的数学原理,那么下面的这些速查表将会是你需要的。概率论
链接: http://www.wzchen.com/s/probability_cheatsheet.pdf![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/f9e3323b6773f6bfa9db598a13819f38.jpg)
线性代数
链接: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/446e7c4fd1a55edaf101eb797fd8e08c.jpg)
统计学
链接: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/9b6add5ea43a27a3846b1afba37c3090.jpg)
微积分
链接: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/ed62bf819d71b340654a80f375b59cbd.jpg)
打包下载:https://pan.baidu.com/s/1mi0viGS
![](https://oscdn.geek-share.com/Uploads/Images/Content/201912/21/2e948c0374175e85709b0cd09ef63aa7.png)
微信扫一扫
关注该公众号
即将打开""小程序
取消打开
相关文章推荐
- 27 个机器学习、数学、Python 速查表
- 27 个机器学习、数学、Python 速查表
- 27 个机器学习、数学、Python 速查表
- 关于 Python 数据抓取、分析、挖掘、机器学习和Python 分布式计算内容分享
- 【备忘】冲击年薪50万之从数学基础python机器学习到深度学习算法学习路线视频教程
- 分享:探索 Python、机器学习和 NLTK 库
- 机器学习:吴恩达Coursera机器学习课后作业答案MATLAB和Python版本分享
- 机器学习基础(二十)—— 数学语言与 Python 代码
- 机器学习、Python和数学学习资料汇总
- 全机器学习和Python的27个速查表(完整版)
- 关于 Python 数据抓取 & 分析 & 机器学习 & 挖掘 & 神经网络 内容的分享。
- 500G python web、爬虫、数据分析、机器学习、大数据、前端实战项目视频代码免费分享
- 【备忘】冲击年薪50万之从数学基础python机器学习到深度学习算法学习路线视频教程 共321G
- 留学生作业代写 编程代写 有偿代写 python matlab数学建模 机器学习 深度学习 c# c++ 数学 算法 论文程序代写
- 机器学习数学|偏度与峰度及其python实现
- [机器学习]机器学习之Python之NumPy数学库的介绍
- 机器学习---Logistic回归数学推导以及python实现
- 机器学习---Python数学做图
- 模型汇总22 机器学习相关基础数学理论、概念、模型思维导图分享
- 数学模型 机器学习 系统聚类(system clustering) Python实现