| 书目名称 | Python for Probability, Statistics, and Machine Learning | | 编辑 | José Unpingco | | 视频video | http://file.papertrans.cn/765/764248/764248.mp4 | | 概述 | Features fully updated explanation on how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods.New edition features Python version 3.7 and connects | | 图书封面 |  | | 描述 | This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. 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. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras 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 updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient d | | 出版日期 | Textbook 20192nd edition | | 关键词 | IPython Notebooks; Machine Learning; Probability and Statistics; Scientific Python; Open Source Python T | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-030-18545-9 | | isbn_softcover | 978-3-030-18547-3 | | isbn_ebook | 978-3-030-18545-9 | | copyright | Springer Nature Switzerland AG 2019 |
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