Computational Neural Networks for Geophysical Data Processing is popular PDF and ePub book, written by M.M. Poulton in 2001-06-13, it is a fantastic choice for those who relish reading online the Science genre. Let's immerse ourselves in this engaging Science book by exploring the summary and details provided below. Remember, Computational Neural Networks for Geophysical Data Processing can be Read Online from any device for your convenience.

Computational Neural Networks for Geophysical Data Processing Book PDF Summary

This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances in network design, data set design, testing strategy, and error analysis. Computational, rather than artificial, modifiers are used for neural networks in this book to make a distinction between networks that are implemented in hardware and those that are implemented in software. The term artificial neural network covers any implementation that is inorganic and is the most general term. Computational neural networks are only implemented in software but represent the vast majority of applications. While this book cannot provide a blue print for every conceivable geophysics application, it does outline a basic approach that has been used successfully.

Detail Book of Computational Neural Networks for Geophysical Data Processing PDF

Computational Neural Networks for Geophysical Data Processing
  • Author : M.M. Poulton
  • Release : 13 June 2001
  • Publisher : Elsevier
  • ISBN : 9780080529653
  • Genre : Science
  • Total Page : 351 pages
  • Language : English
  • PDF File Size : 17,7 Mb

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