fossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 21 hours agoSquiggly Boiemander.xyzimagemessage-square34fedilinkarrow-up1628arrow-down19
arrow-up1619arrow-down1imageSquiggly Boiemander.xyzfossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 21 hours agomessage-square34fedilink
minus-squareBeeegScaaawyCripple@lemmy.worldlinkfedilinkEnglisharrow-up5·14 hours agoohhhh so that’s The model for neural networks, not A model for neural networks
minus-squareNotANumber@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up2·2 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-up4·6 hours 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-up2·edit-216 minutes 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.