fossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 2 days agoSquiggly Boiemander.xyzimagemessage-square62fedilinkarrow-up1822arrow-down111
arrow-up1811arrow-down1imageSquiggly Boiemander.xyzfossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 2 days agomessage-square62fedilink
minus-squareBeeegScaaawyCripple@lemmy.worldlinkfedilinkEnglisharrow-up5·1 day agoohhhh so that’s The model for neural networks, not A model for neural networks
minus-squareNotANumber@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up5·22 hours agoIt’s only one type of neural network. A dense MLP. You have sparse neural networks, recurrent neural networks, convolutional neural networks and more!
minus-squareTamo240@programming.devlinkfedilinkEnglisharrow-up8·1 day agoIts an abstraction for neural networks. Different individual networks might vary in number of layers (columns), nodes (circles), or loss function (lines), but the concept is consistent across all.
minus-squareNotANumber@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up4·edit-220 hours agoKinda but also no. That’s specifically a dense neural network or MLP. It gets a lot more complicated than that in some cases.
ohhhh so that’s The model for neural networks, not A model for neural networks
It’s only one type of neural network. A dense MLP. You have sparse neural networks, recurrent neural networks, convolutional neural networks and more!
Its an abstraction for neural networks. Different individual networks might vary in number of layers (columns), nodes (circles), or loss function (lines), but the concept is consistent across all.
Kinda but also no. That’s specifically a dense neural network or MLP. It gets a lot more complicated than that in some cases.