27 个机器学习、数学、Python 速查表

机器学习
标签: #<Tag:0x00007faff2f39810>

(iQuant) #1

导语: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。

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/

$$图1 \ \ The Neural \ Network\ Zoo $$

Microsoft Azure Algorithm Flowchart

链接: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet

$$图2 \ Machine \ learning \ algorithm \ cheat \ sheet\ for\ Microsoft \ Azure \ Machine \ Learning \ Studio$$

SAS Algorithm Flowchart

链接: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/

$$图3 \ SAS: Which\ machine\ learning \ algorithm\ should\ I\ use?$$

Algorithm Summary

链接: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

$$图4 \ \ Algorithm \ Summary $$
###A Tour of Machine Learning Algorithms

$$图5 \ Which\ are \ the\ best\ known\ machine \ learning\ algorithms?$$

Algorithm Pro/Con

链接: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend


$$图6 \ \ Algorithm\ Pro/Con $$
##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/

$$图7 \ \ ML算法 $$

Python基础

链接: http://datasciencefree.com/python.pdf

$$图8 \ Python\ Basic \ Cheat \ Sheet$$
链接: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA

$$图9 \ Python\ For \ Data \ Science \ Cheat \ Sheet$$

Numpy

链接: https://www.dataquest.io/blog/numpy-cheat-sheet/

$$图10 \ Numpy \ Basic \ Cheat \ Sheet$$
链接: http://datasciencefree.com/numpy.pdf

$$图11 \ Numpy \ Cheat \ Sheet \ with \ Python \ Package $$

链接: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE

$$图12 \ Python\ For \ Data \ Science \ Cheat \ Sheet \ Numpy \ Basic $$

链接: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb

$$图13 \ \ Numpy \ Basic $$

Pandas

链接: http://datasciencefree.com/pandas.pdf

$$图14 \ \ Data \ Analysis \ with \ Pandas \ Cheat \ Sheet $$

链接: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U

$$图15 \ Python\ For \ Data \ Science \ Cheat \ Sheet \ Pandas \ Basic $$

链接: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb

$$图16 \ Pandas \ Basic$$

Matplotlib

链接: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet

$$图17\ Python\ For \ Data \ Science \ Cheat \ Sheet \ Matplotlib $$

链接: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb

$$图18 \ Matplotlib \ Basic$$

Scikit Learn

链接: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk

$$图19 \ Python\ For \ Data \ Science \ Cheat \ Sheet \ Scikit-Learn $$
链接: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html

$$图20 \ Scikit-Learn \ Algorithm \ Cheat \ Sheet $$
链接: https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb

$$图21 \ Scikit-Learn \ Basic $$

Tensorflow

链接: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb

$$图22 \ \ Tensorflow \ Basic $$

Pytorch

链接: https://github.com/bfortuner/pytorch-cheatsheet


$$图23 \ \ Pytorch \ Basic $$

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

$$图24 \ \ Probability \ Cheat \ Sheet $$

线性代数

链接: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf

$$图25 \ \ Linear \ algebra \ Cheat \ Sheet $$

统计学

链接: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf


$$图26 \ \ Statistics \ Cheat \ Sheet $$

微积分

链接: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N


$$图27 \ \ Calculus \ Cheat \ Sheet $$


numpy 、pandas 和Matplotlib基本用法的整理与汇总