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Hybrid imaging and visualizationemploying machine learning with Mathematica - Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hybrid imaging and visualizationby 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.
Contained By:	Springer Nature eBook
Subject:	Computer vision.
Online resource:	https://doi.org/10.1007/978-3-031-72817-4
ISBN:	9783031728174$q(electronic bk.)

Hybrid imaging and visualizationemploying 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$q(electronic bk.)

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

200113
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

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