Hybrid Imaging and Visualization Employing Machine Learning with Mathematica Python 2nd Edition By Joseph Awange, Béla Paláncz, Lajos Völgyesi
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Hybrid Imaging and Visualization Employing Machine Learning with Mathematica Python 2nd Edition By Joseph Awange, Béla Paláncz, Lajos Völgyesi

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Pages
471
File size
15.57 MB
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Digital PDF
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eBook[PDF]
About this ebook
Hybrid Imaging and Visualization: Employing Machine Learning with Mathematica - Python (2nd Edition)" is an advanced academic textbook authored by Joseph Awange, Béla Paláncz, and Lajos Völgyesi. Published by Springer, the book bridges computer algebra, machine learning, and advanced geoinformatics. [1, 2]
Core Focus
The book blends symbolic-numeric computations with deep learning. It provides parallel implementations in Wolfram Mathematica and Python. This allows readers to compare symbolic workflows with traditional programming. [1, 2, 5]
New Features in the 2nd Edition
The second edition expands heavily into modern artificial intelligence and optimization techniques: [, 2]
  • The Black Hole Algorithm: Introduced for advanced hyperparameter optimization in machine learning workflows.
  • Generative AI & ChatGPT: Explores the integration and capabilities of modern Large Language Models within data tasks.
  • Time-Series to Image Conversion: Techniques to transform sequential data into images to utilize Convolutional Neural Networks (CNNs) for classification.
  • Fisher Discriminant Analysis: Enhanced optimal object separation methods. [1]
Target Audience
The text targets researchers, data scientists, and engineers in computer vision, geophysics, and spatial sciences. It helps professionals who require robust mathematical modeling alongside machine learning. [, 2, 3]

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Topics

advanced hyperparameter optimization in machine learning workflows compare symbolic workflows with traditional programming computer algebra machine learning and advanced geoinformatics computer vision geophysics and spatial sciences Fisher Discriminant Analysis: Enhanced optimal object separation methods Generative AI & ChatGPT implementations in Wolfram Mathematica and Python integration and capabilities of modern Large Language Models symbolic-numeric computations The Black Hole Algorithm Time-Series to Image Conversion transform sequential data into images to utilize Convolutional Neural Networks (CNNs)
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