Название: Hybrid Imaging and Visualization: Employing Machine Learning with Mathematica - Python, 2nd Edition
Автор: Joseph Awange, Béla Paláncz, Lajos Völgyesi
Издательство: Springer
Год: 2025
Страниц: 471
Язык: английский
Формат: pdf (true)
Размер: 65.4 MB
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. Computer vision, a multidisciplinary field that is broadly a subfield of artificial intelligence and Machine Learning has as one of its goals the extraction of useful information from images. A basic problem in computer vision, therefore, is to try to understand, i.e., “see” the structure of the real world from a given set of images through use of specialized methods and general learning algorithms. In this book, we employ Python and Mathematica as well as their blending, since Python code can be run from Mathematica directly.
