標(biāo)題: Titlebook: Numerical Python; Scientific Computing Robert Johansson Book 20192nd edition Robert Johansson 2019 Python.numerical.NumPy.SciPy.computation [打印本頁] 作者: centipede 時(shí)間: 2025-3-21 18:34
書目名稱Numerical Python影響因子(影響力)
書目名稱Numerical Python影響因子(影響力)學(xué)科排名
書目名稱Numerical Python網(wǎng)絡(luò)公開度
書目名稱Numerical Python網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Numerical Python被引頻次
書目名稱Numerical Python被引頻次學(xué)科排名
書目名稱Numerical Python年度引用
書目名稱Numerical Python年度引用學(xué)科排名
書目名稱Numerical Python讀者反饋
書目名稱Numerical Python讀者反饋學(xué)科排名
作者: IDEAS 時(shí)間: 2025-3-21 21:48 作者: GIDDY 時(shí)間: 2025-3-22 03:04 作者: NAVEN 時(shí)間: 2025-3-22 05:58 作者: 噴出 時(shí)間: 2025-3-22 10:10
Robert Johansson 2019作者: Repetitions 時(shí)間: 2025-3-22 14:47 作者: 平 時(shí)間: 2025-3-22 17:44 作者: SHRIK 時(shí)間: 2025-3-22 22:09
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.作者: 軌道 時(shí)間: 2025-3-23 01:22
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.作者: 2否定 時(shí)間: 2025-3-23 05:39
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).作者: Dictation 時(shí)間: 2025-3-23 11:11 作者: 乏味 時(shí)間: 2025-3-23 15:07
Signal Processing,nuous functions, but in computational applications, it is common to work with discretized signals, where the original continuous signal is sampled at discrete points with uniform distances. The sampling theorem gives rigorous and quantitative conditions for when a continuous signal can be accurately represented by a discrete sequence of samples.作者: 神化怪物 時(shí)間: 2025-3-23 21:29 作者: 越自我 時(shí)間: 2025-3-23 23:00
Symbolic Computing,t is possible to take analytical computing much further than can realistically be done by hand. Symbolic computing is a great tool for checking and debugging analytical calculations that are done by hand, but more importantly it enables carrying out analytical analysis that may not otherwise be possible.作者: 樣式 時(shí)間: 2025-3-24 05:38 作者: antiandrogen 時(shí)間: 2025-3-24 07:52 作者: 約會(huì) 時(shí)間: 2025-3-24 11:30
numerical Python packages like NumPy, FiPy, Pillow, matplot.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 d作者: mettlesome 時(shí)間: 2025-3-24 16:01 作者: 祝賀 時(shí)間: 2025-3-24 19:05 作者: 親愛 時(shí)間: 2025-3-25 00:42 作者: 分解 時(shí)間: 2025-3-25 05:52 作者: 專心 時(shí)間: 2025-3-25 08:52 作者: Hectic 時(shí)間: 2025-3-25 14:31 作者: expound 時(shí)間: 2025-3-25 19:45
Vectors, Matrices, and Multidimensional Arrays,values, it is natural and advantageous to represent the data as arrays and the computation in terms of array operations. Computations that are formulated this way are said to be vectorized. Vectorized computing eliminates the need for many explicit loops over the array elements by applying batch ope作者: 現(xiàn)暈光 時(shí)間: 2025-3-25 23:04
Symbolic Computing,In symbolic computing software, also known as computer algebra systems (CASs), representations of mathematical objects and expressions are manipulated and transformed analytically. Symbolic computing is mainly about using computers to automate analytical computations that can in principle be done by作者: Promotion 時(shí)間: 2025-3-26 01:58
Plotting and Visualization,he end product of nearly all computations?– be it numeric or symbolic?– is a plot or a graph of some sort. It is when visualized in graphical form that knowledge and insights can be most easily gained from computational results. Visualization is therefore a tremendously important part of the workflo作者: COKE 時(shí)間: 2025-3-26 05:52 作者: 功多汁水 時(shí)間: 2025-3-26 12:27
Interpolation,uld exactly coincide with the given data points, and it can also be evaluated for other intermediate input values within the sampled range. There are many applications of interpolation: A typical use-case that provides an intuitive picture is the plotting of a smooth curve through a given set of dat作者: HEAVY 時(shí)間: 2025-3-26 14:32
Integration, is also known as .. Integration is significantly more difficult than its inverse operation?– differentiation?– and while there are many examples of integrals that can be calculated analytically, in general we have to resort to numerical methods. Depending on the properties of the integrand (the fun作者: Judicious 時(shí)間: 2025-3-26 17:20
Ordinary Differential Equations,rential equations. An . differential equation is a special case where the unknown function has only one independent variable with respect to which derivatives occur in the equation. If, on the other hand, derivatives of more than one variable occur in the equation, then it is known as a . differenti作者: 下級(jí) 時(shí)間: 2025-3-27 00:28 作者: allude 時(shí)間: 2025-3-27 02:59
Partial Differential Equations,e derivatives in the equation are . derivatives. As such they are generalizations of ordinary differential equations, which were covered in Chapter .. Conceptually, the difference between ordinary and partial differential equations is not that big, but the computational techniques required to deal w作者: Hemoptysis 時(shí)間: 2025-3-27 06:45
Data Processing and Analysis,ional work. Starting with this chapter, we move on to explore data processing and analysis, statistics, and statistical modeling. As the first step in this direction, we look at the data analysis library .. This library provides convenient data structures for representing series and tables of data a作者: 商議 時(shí)間: 2025-3-27 10:09
Statistics,s, medicine, and other fields where data is used for obtaining knowledge and making decisions. With the recent proliferation of data analytics, there has been a surge of renewed interest in statistical methods. Still, computer-aided statistics has a long history, and it is a field that traditionally作者: 真 時(shí)間: 2025-3-27 14:51
Statistical Modeling,r and explore statistical modeling, which deals with creating models that attempt to explain data. A model can have one or several parameters, and we can use a fitting procedure to find the values of the parameter that best explains the observed data. Once a model has been fitted to data, it can be 作者: Sleep-Paralysis 時(shí)間: 2025-3-27 17:52 作者: 繁重 時(shí)間: 2025-3-27 22:07
Bayesian Statistics,ayesian statistics, in contrast to the frequentist’s statistics that we used in Chapter . and Chapter ., treats probability as a degree of belief rather than as a measure of proportions of observed outcomes. This different point of view gives rise to distinct statistical methods that we can use in p作者: Dappled 時(shí)間: 2025-3-28 02:56
Signal Processing,ntext can be a quantity that varies in time (temporal signal) or as a function of space coordinates (spatial signal). For example, an audio signal is a typical example of a temporal signal, while an image is a typical example of a spatial signal in two dimensions. In reality, signals are often conti作者: 敵手 時(shí)間: 2025-3-28 06:16 作者: 滔滔不絕的人 時(shí)間: 2025-3-28 12:59 作者: BUDGE 時(shí)間: 2025-3-28 17:50 作者: 明確 時(shí)間: 2025-3-28 18:59
le I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning978-1-4842-4246-9作者: insurgent 時(shí)間: 2025-3-28 23:17
Integration,grals (double integrals) and higher-order integrals can be numerically computed with repeated single-dimension integration or using methods that are multidimensional generalizations of the techniques used to solve single-dimensional integrals. However, the computational complexity grows quickly with作者: 協(xié)迫 時(shí)間: 2025-3-29 06:18 作者: synovium 時(shí)間: 2025-3-29 07:38
Data Processing and Analysis,ch, the . library has become a de facto standard library for high-level data processing in Python, especially for statistics applications. The . library itself contains only limited support for statistical modeling (namely, linear regression). For more involved statistical analysis and modeling, the作者: 痛苦一生 時(shí)間: 2025-3-29 12:05
Statistics, of statistics, albeit not all, while also providing the unique advantages of the Python programming language and its environment. The pandas library that we discussed in Chapter . is an example of a development within the Python community that was strongly influenced by statistical software, with t作者: Receive 時(shí)間: 2025-3-29 19:08 作者: 尊重 時(shí)間: 2025-3-29 23:40
Book 20192nd editionng 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作者: 鬧劇 時(shí)間: 2025-3-30 03:53 作者: FEIGN 時(shí)間: 2025-3-30 04:05
7樓作者: Hectic 時(shí)間: 2025-3-30 08:17
7樓作者: 星星 時(shí)間: 2025-3-30 15:08
8樓作者: 繁重 時(shí)間: 2025-3-30 20:11
8樓作者: 戰(zhàn)役 時(shí)間: 2025-3-31 00:41
8樓作者: 擁擠前 時(shí)間: 2025-3-31 04:42
8樓作者: Parabola 時(shí)間: 2025-3-31 05:22
9樓作者: 倔強(qiáng)不能 時(shí)間: 2025-3-31 11:02
9樓作者: 心神不寧 時(shí)間: 2025-3-31 13:41
9樓作者: acclimate 時(shí)間: 2025-3-31 19:24
9樓作者: 駁船 時(shí)間: 2025-3-31 22:23
10樓作者: 發(fā)炎 時(shí)間: 2025-4-1 02:44
10樓作者: 過濾 時(shí)間: 2025-4-1 06:29
10樓作者: 無法破譯 時(shí)間: 2025-4-1 13:58
10樓
