Hybrid Imaging and Visualization
Employing Machine Learning with Mathematica - Python
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Beschreibung
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.
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.
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
Autor*in
Joseph Awange
Themen in »Hybrid Imaging and Visualization«
Computer Vision Computational Algorithms for Visualization Convolution Neural Networks Data Visualization Hybrid Imaging Hybrid Symbolic-Numeric Computations Hybrid Visualization Image Compression Image Recognition Laser Scanning Photogrammetry Remote Sensing/Photogrammetry Restoration Images Symbolic-Numeric Computation