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通过线段和圆完整度对图像进行圆检测” ICIP 2016中论文“通过线段和圆完整度对图像进行圆检测”的代码和CDBD 如果您认为该代码很有用，请引用我们的工作 @article{CDBD, author = {Truc Le and Ye Duan}, title = {Circle Detection on Images by Line Segment and Circle Completeness}, journal = {IEEE International Conference on Image Processing (ICIP)}, month = {September}, year = {2016}, pages = {3648--3652}, }

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This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

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