Название: Hybrid Imaging and Visualization: Employing Machine Learning with Mathematica - Python, 2nd EditionАвтор: Joseph Awange, Béla Paláncz, Lajos Völgyesi
Издательство: Springer
Год: 2025
Страниц: 471
Язык: английский
Формат: pdf
Размер: 62.9 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. It is therefore appropriate to provide a brief discussion on them. This section is thus dedicated to their exposition. Python is now undoubtedly the most popular language for data science projects, while the Wolfram Language is rather a niche language in this concern. Consequently, Python is probably well-known to the reader compared to Mathematica. Given that Wolfram Language, widely used in academia (especially in physics, mathematics and financial analytics) has been around for over 30 years, it is actually older than both R and Python.
- Presents an efficient supervised learning classifier for dealing with nonlinear separations
- Discusses optimization hyperparameters, which are very important for efficient use of Machine Learning algorithms
- Introduces ChatGPT, the most popular AI topic, and discusses its principle and applications
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