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Hybrid imaging and visualization = e...

Awange, Joseph L.

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Hybrid imaging and visualization = employing machine learning with Mathematica - Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hybrid imaging and visualization/ by Joseph Awange, Béla Paláncz, Lajos Völgyesi.
Reminder of title:	employing machine learning with Mathematica - Python /
Author:	Awange, Joseph L.
other author:	Paláncz, Béla.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xxiii, 450 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1. Dimension Reduction -- Chapter 2. Classification -- Chapter 3. Clustering -- Chapter 4. Regression -- Chapter 5. Neural Networks -- Chapter 6. Optimizing Hyperparameters -- Chapter 7. ChatGPT.
Contained By:	Springer Nature eBook
Subject:	Computer vision. -
Online resource:	https://doi.org/10.1007/978-3-031-72817-4
ISBN:	9783031728174

Hybrid imaging and visualization = employing machine learning with Mathematica - Python /
Awange, Joseph L.

Hybrid imaging and visualizationemploying machine learning with Mathematica - Python /[electronic resource] :by Joseph Awange, Béla Paláncz, Lajos Völgyesi. - Second edition. - Cham :Springer Nature Switzerland :2025. - xxiii, 450 p. :ill. (some col.), digital ;24 cm.

Chapter 1. Dimension Reduction -- Chapter 2. Classification -- Chapter 3. Clustering -- Chapter 4. Regression -- Chapter 5. Neural Networks -- Chapter 6. Optimizing Hyperparameters -- Chapter 7. ChatGPT.

This second edition of the book that targets those in computer algebra and artificial intelligence introduces Black Hole algorithm that is essential for optimizing hyperparameters, an important task in machine learning where mostly, stochastic global methods are used as well as ChatGPT, a novel and in the last few years, very popular Generative AI technology. In addition, fisher discriminant, a linear discriminant that can provide an optimal separation of objects, and the conversion of time series into images thereby making it possible to employ convolution neural network to classify time series effectively are presented.

ISBN: 9783031728174

Standard No.: 10.1007/978-3-031-72817-4doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Hybrid imaging and visualization = employing machine learning with Mathematica - Python /
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245 1 0 $a Hybrid imaging and visualization $h [electronic resource] : $b employing machine learning with Mathematica - Python / $c by Joseph Awange, Béla Paláncz, Lajos Völgyesi.
250 $a Second edition.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xxiii, 450 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter 1. Dimension Reduction -- Chapter 2. Classification -- Chapter 3. Clustering -- Chapter 4. Regression -- Chapter 5. Neural Networks -- Chapter 6. Optimizing Hyperparameters -- Chapter 7. ChatGPT.
520 $a This second edition of the book that targets those in computer algebra and artificial intelligence introduces Black Hole algorithm that is essential for optimizing hyperparameters, an important task in machine learning where mostly, stochastic global methods are used as well as ChatGPT, a novel and in the last few years, very popular Generative AI technology. In addition, fisher discriminant, a linear discriminant that can provide an optimal separation of objects, and the conversion of time series into images thereby making it possible to employ convolution neural network to classify time series effectively are presented.
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650 2 4 $a Geophysics. $3 535228
650 2 4 $a Space Physics. $3 3538914
700 1 $a Paláncz, Béla. $3 3783582
700 1 $a Völgyesi, Lajos. $3 3783583
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