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机器学习 这里有一些有用的流程图和机器学习算法表,我只包括了我所发现的最全面的几个。 神经网络架构 (via:http://www./neural-network-zoo/) 神经网络公园 微软 Azure 算法流程图 (via:https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet) 用于微软 Azure 机器学习工作室的机器学习算法 SAS 算法流程图 (via:http://blogs./content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/) SAS:我应该使用哪个机器学习算法? 算法总结 (via:http:///a-tour-of-machine-learning-algorithms/) 机器学习算法指引 (via:http:///best-known-machine-learning-algorithms-infographic/) 已知的机器学习算法哪个最好? 算法优劣 (via: https://blog./machine-learning-explained-algorithms-are-your-friend) Python 自然而然,也有许多在线资源是针对 Python 的,这一节中,我仅包括了我所见过的最好的那些小抄。 算法 (via:https://www./blog/2015/09/full-cheatsheet-machine-learning-algorithms/) Python 基础 (via:http:///python.pdf) 数据科学Python入门备忘单 (via:https://www./community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA) NumPy Cheat Sheet - Python for Data Science (via:https://www./blog/numpy-cheat-sheet/) Numpy Cheat Sheet (via: http:///numpy.pdf) NumPy Cheat Sheet: Data Analysis in Python (via:https://www./community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE) Data-Science-Ipython-Notebooks(NumPy) (via:https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/ numpy/numpy.ipynb) Data Analysis with Pandas (via:http:///pandas.pdf) Pandas Cheat Sheet for Data Science in Python (via:https://www./community/blog/python-pandas-cheat-sheet#gs. S4P4T=U) Data-Science-Ipython-Notebooks(Pandas) (via:https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/ pandas/pandas.ipynb) Matplotlib Cheat Sheet: Plotting in Python (via:https://www./community/blog/python-matplotlib-cheat-sheet) Data-Science-Ipython-Notebooks (via: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/ matplotlib/matplotlib.ipynb) Scikit Learn (via: https://www./community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk) Machine Learning Cheat Sheet (for scikit-learn) (via:http://peekaboo-vision./2013/01/machine-learning-cheat-sheet-for-scikit.html) ml_cheat_sheet (via: https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning. ipynb) TensorFlow-Examples (via: https://github.com/aymericdamien/TensorFlow-Examples/blob/ master/notebooks/1_Introduction/basic_operations.ipynb) Pytorch Cheatsheet (via: https://github.com/bfortuner/pytorch-cheatsheet) 数学 如果你希望了解机器学习,那你就需要彻底地理解统计学(特别是概率)、线性代数和一些微积分。我在本科时辅修了数学,但是我确实需要复习一下了。这些小抄提供了机器学习算法背后你所需要了解的大部分数学知识。 概率 (via:http://www./s/probability_cheatsheet.pdf) 概率小抄 2.0 线性代数 (via: https:///static/tutorials/linear_algebra_in_4_pages.pdf) 四页内解释线性代数 统计学 (via: http://web./~csvoss/Public/usabo/stats_handout.pdf) 统计学小抄 微积分 (via:http://tutorial.math./getfile.aspx?file=B,41,N) 微积分小抄
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