We compare different machine learning estimators and present details about their implementation in python. The computational studies are conducted for classification as well as regression problems. Moreover, as one of...
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We compare different machine learning estimators and present details about their implementation in python. The computational studies are conducted for classification as well as regression problems. Moreover, as one of the founding problems of machine learning, we present the specific classification task of handwritten digit recognition. In this connection, we discuss the mathematical formulation and of course the implementation details of this problem. All corresponding python code is fully provided on request and can be downloaded from the author's GitHub page https://***/Fab1Fatal.
This study examined how the principles of visual rhetoric are incorporated into python programming. It analyzes the effects of visual elements, such as syntax highlighting, indentation, and comments, on readability an...
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ISBN:
(纸本)9798400705199
This study examined how the principles of visual rhetoric are incorporated into python programming. It analyzes the effects of visual elements, such as syntax highlighting, indentation, and comments, on readability and comprehension of code. The findings suggest pedagogical strategies for TPC instructors to emphasize visual rhetoric principles when teaching python programming, with the aim of improving the interaction between humans and computers.
Multidimensional scaling (MDS) is a widely used technique for mapping data from a high-dimensional to a lower-dimensional space and for visualizing data. Recently, a new method, known as Geometric MDS, has been develo...
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Multidimensional scaling (MDS) is a widely used technique for mapping data from a high-dimensional to a lower-dimensional space and for visualizing data. Recently, a new method, known as Geometric MDS, has been developed to minimize the MDS stress function by an iterative procedure, where coordinates of a particular point of the projected space are moved to the new position defined analytically. Such a change in position is easily interpreted geometrically. Moreover, the coordinates of points of the projected space may be recalculated simultaneously, i.e. in parallel, independently of each other. This paper has several objectives. Two implementations of Geometric MDS are suggested and analysed experimentally. The parallel implementation of Geometric MDS is developed for multithreaded multi-core processors. The sequential implementation is optimized for computational speed, enabling it to solve large data problems. It is compared with the SMACOF version of MDS. python codes for both Geometric MDS and SMACOF are presented to highlight the differences between the two implementations. The comparison was carried out on several aspects: the comparative performance of Geometric MDS and SMACOF depending on the projection dimension, data size and computation time. Geometric MDS usually finds lower stress when the dimensionality of the projected space is smaller.
python codes (link) Among the many branches of applied mathematics, options pricing theory occupies a unique position: it utilizes a wide range of advanced mathematical concepts, making it appealing to mathematicians...
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Among the many branches of applied mathematics, options pricing theory occupies a unique position: it utilizes a wide range of advanced mathematical concepts, making it appealing to mathematicians, and it is regularly applied at financial institutions, making it indispensable to practitioners. The emergence of artificial intelligence in the financial industry has led to further interest in mathematical finance and has increased the demand for literature on this subject that is accessible to a large audience.
This book presents a self-contained introduction to options pricing theory and includes
a complete discussion of the required concepts in finance and probability theory;
an introduction to basic models, emphasizing both critical thinking and practical applications
over 200 exercises, several python codes for the analysis and application of the options pricing models, and numerical projects intended to help close the gap between theory and practice.
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