Convolutional Neural Networks Architecture: A Tutorial
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Authors
Avalos, Sebastian
Ortiz, Julian M.
Date
2019
Type
journal article
Language
en
Keyword
Alternative Title
Abstract
Deep learning techniques have found an increasing number of applications in the field of geosciences. Among the most applied ones, Convolutional Neural Networks stand out by their ability to extract features from grid-like topological inputs, enriching the information fed to prediction models, improving their accuracies. This tutorial seeks to explain step by step the building blocks of Convolutional Neural Networks and how their inner parameters are trained in order to effectively extract features.
Description
this is a preprint version of a paper that was subsequently used as a basis for: M. Bolgkoranou and J. M. Ortiz, “Multivariate Geostatistical Simulation using Principal Component Analysis.”. It does not contain changes and edits that were made during peer review, or updates, edits and alterations by the authors and the publisher that occurred prior to acceptance and final publication
Citation
Avalos S, Ortiz JM (2019) Convolutional neural networks architecture: A tutorial, Predictive Geometallurgy and Geostatistics Lab, Queen’s University, Annual Report 2019, paper 2019-12, 159-168.
Publisher
Queen's University