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Why are convolutional NNs (or networks with several Convolutional layers as opposed to none) more useful especially for images, than networks with only fully connected layers? You mention something about translational equivariance in artificial NNs and in the visual cortex in brains, but this is a property of the neural network, not of its inputs. It's a way to reduce the number of weights per layer, but why isn't it universally useful (for all inputs and all output tasks), and why is it better for images than other ways of reducing the number of weights per layer? [[User:Iuvalclejan|Iuvalclejan]] ([[User talk:Iuvalclejan|talk]]) 23:50, 25 January 2025 (UTC)
==Wiki Education assignment: Linguistics in the Digital Age==
{{dashboard.wikiedu.org assignment | course = Wikipedia:Wiki_Ed/University_of_Arizona/Linguistics_in_the_Digital_Age_(Spring_2025) | assignments = [[User:AshlaMaOmao|AshlaMaOmao]] | start_date = 2025-01-15 | end_date = 2025-05-07 }}
<span class="wikied-assignment" style="font-size:85%;">— Assignment last updated by [[User:Aiden304|Aiden304]] ([[User talk:Aiden304|talk]]) 21:15, 26 March 2025 (UTC)</span>
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