27 个机器学习、数学、Python 速查表

   <p>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。</p>    <p>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。</p>    <p>如果你们像我一样想要一次性批量下载所有资源，我已经将 27 个速查表整理打包（ <a href="/misc/goto?guid=4959751108002137985" rel="nofollow,noindex">Dropbox</a> 、 <a href="/misc/goto?guid=4959751108089693186" rel="nofollow,noindex">百度云</a> ）好了，请尽情享用吧！</p>    <p>如果你喜欢本文，记得给我在下面点个 zan 哦。</p>    <h2>机器学习</h2>    <p>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。</p>    <h3>Neural Network Architectures</h3>    <p style="text-align:center">链接: <a href="/misc/goto?guid=4959749430917032492" rel="nofollow,noindex">http://www.asimovinstitute.org/neural-network-zoo/</a> <img src="https://simg.open-open.com/show/d6a26b41e5f4b980e57ae61434dbfc1e.png"></p>    <p style="text-align:center">The Neural Network Zoo</p>    <h3>Microsoft Azure Algorithm Flowchart</h3>    <p>链接: <a href="/misc/goto?guid=4959751108204732310" rel="nofollow,noindex"> https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/91f3525fb999dab8372b9d720d569697.png"></p>    <p style="text-align:center">Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio</p>    <h3>SAS Algorithm Flowchart</h3>    <p>链接: <a href="/misc/goto?guid=4959751108285287933" rel="nofollow,noindex"> http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/ </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/a294c10effecc494735850ccb8c8ba16.png"></p>    <p style="text-align:center">SAS: Which machine learning algorithm should I use?</p>    <h3>Algorithm Summary</h3>    <p>链接: <a href="/misc/goto?guid=4958978647652973714" rel="nofollow,noindex"> http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/ </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/d93eec3173586733121b778eec57dd67.png"></p>    <p style="text-align:center">A Tour of Machine Learning Algorithms</p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/9bedfc4dd0e15468e4350b8d86ab0d0f.jpg"></p>    <p style="text-align:center"><em>Which are the best known machine learning algorithms?</em></p>    <h3>Algorithm Pro/Con</h3>    <p>链接: <a href="/misc/goto?guid=4959751108394373362" rel="nofollow,noindex"> https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/b9cd1b919991f94e6e4fee018d64f406.jpg"></p>    <h2>Python</h2>    <p>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。</p>    <h3>ML算法</h3>    <p>链接: <a href="/misc/goto?guid=4959751108469691683" rel="nofollow,noindex"> https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/ </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/3cf81de0802fdf1e25126a1995e6f85e.png"></p>    <h3>Python基础</h3>    <p>链接: <a href="/misc/goto?guid=4959751108565305880" rel="nofollow,noindex"> http://datasciencefree.com/python.pdf </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/3c27dca38318cc6f9b35f629c4d0cdf5.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751108638146131" rel="nofollow,noindex"> https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/b31d8c3df7f530cf8db646e87a499b94.png"></p>    <h3>Numpy</h3>    <p>链接: <a href="/misc/goto?guid=4959751108722049440" rel="nofollow,noindex"> https://www.dataquest.io/blog/numpy-cheat-sheet/ </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/442881e01990abe0206e588b6a6ebcfc.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751108795374381" rel="nofollow,noindex"> http://datasciencefree.com/numpy.pdf </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/fb577f69085892d1eff6833565f6bac0.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751108884221855" rel="nofollow,noindex"> https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/47001e7331173217be7a6fe454b4d983.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751108955992999" rel="nofollow,noindex"> https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/03573b92f37b0d32b44194d2eca2f4bc.png"></p>    <h3>Pandas</h3>    <p>链接: <a href="/misc/goto?guid=4959751109057300345" rel="nofollow,noindex"> http://datasciencefree.com/pandas.pdf </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/25d1f359bf7c8aa1e4d662776fd57a9a.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751109137191293" rel="nofollow,noindex"> https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/c92d7fd090979610ba3d457e886412b6.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751109220395971" rel="nofollow,noindex"> https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/1620b69eab74a809633a2b5b852776be.png"></p>    <h3>Matplotlib</h3>    <p>链接: <a href="/misc/goto?guid=4959751109289663512" rel="nofollow,noindex"> https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/d5300d81fa97694c7a6bb5c09b7ec967.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751109379913080" rel="nofollow,noindex"> https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/a64e7b8fbc51852c9dc74f78da609bfb.png"></p>    <h3>Scikit Learn</h3>    <p>链接: <a href="/misc/goto?guid=4959751109449994858" rel="nofollow,noindex"> https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/1605daa296f58f7cb85c6de45365edcc.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751109533107156" rel="nofollow,noindex"> http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/9af0a879f6c40bc37f0c7fd067d81508.png"></p>    <p>链接: <a href="/misc/goto?guid=4959751109620553526" rel="nofollow,noindex"> https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/67113df7f92d6ac802413182c7321431.png"></p>    <h3>Tensorflow</h3>    <p>链接: <a href="/misc/goto?guid=4959721148357075750" rel="nofollow,noindex"> https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/99651f5d22432fc0c24dbc7d975725db.png"></p>    <h3>Pytorch</h3>    <p>链接: <a href="/misc/goto?guid=4959751109732785238" rel="nofollow,noindex"> https://github.com/bfortuner/pytorch-cheatsheet </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/7f800d2962489dbe22776a3247f60c8e.png"></p>    <h2>数学</h2>    <p>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。</p>    <h3>概率论</h3>    <p>链接: <a href="/misc/goto?guid=4959751109806262187" rel="nofollow,noindex"> http://www.wzchen.com/s/probability_cheatsheet.pdf </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/1e71165845dcfcdc9e57679207e7a7b9.png"></p>    <h3>线性代数</h3>    <p>链接: <a href="/misc/goto?guid=4959751109888548153" rel="nofollow,noindex"> https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/a020bee96349e9ae9a95b43500184a7c.png"></p>    <h3>统计学</h3>    <p>链接: <a href="/misc/goto?guid=4959751109967402830" rel="nofollow,noindex"> http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf </a></p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/53962a0c3f20cedbfbe08b281866dc26.png"></p>    <p> </p>    <p>来自：http://blog.jobbole.com/112009/</p>  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