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Titlebook: Numerical Python; Scientific Computing Robert Johansson Book 20192nd edition Robert Johansson 2019 Python.numerical.NumPy.SciPy.computation

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發(fā)表于 2025-3-21 18:34:07 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Numerical Python
副標(biāo)題Scientific Computing
編輯Robert Johansson
視頻videohttp://file.papertrans.cn/670/669159/669159.mp4
概述Revised and updated with new examples using the numerical and mathematical modules in Python and its standard library.Understand open source numerical Python packages like NumPy, FiPy, Pillow, matplot
圖書封面Titlebook: Numerical Python; Scientific Computing Robert Johansson Book 20192nd edition Robert Johansson 2019 Python.numerical.NumPy.SciPy.computation
描述.Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.?.Numerical Python, Second Edition., presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis.?.After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning
出版日期Book 20192nd edition
關(guān)鍵詞Python; numerical; NumPy; SciPy; computation; algorithms; FEniCS; TensorFlow; Signal Processing; Image Proces
版次2
doihttps://doi.org/10.1007/978-1-4842-4246-9
isbn_ebook978-1-4842-4246-9
copyrightRobert Johansson 2019
The information of publication is updating

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Robert Johansson 2019
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Optimization,um or maximum of the function, depending on the application and the specific problem. In this chapter we are concerned with the optimization of real-valued functions of one or several variables, which optionally can be subject to a set of constraints that restricts the domain of the function.
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Interpolation,a points. Another use-case is to approximate complicated functions, which, for example, could be computationally demanding to evaluate. In that case, it can be beneficial to evaluate the original function only at a limited number of points and use interpolation to approximate the function when evaluating it for intermediary points.
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Sparse Matrices and Graphs,ons are matrices where most of the elements are zeros. Such matrices are known as ., and they occur in many applications, for example, in connection networks (such as circuits) and in large algebraic equation systems that arise, for example, when solving partial differential equations (see Chapter . for examples).
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