fossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 2 months agoSquiggly Boiemander.xyzimagemessage-square67fedilinkarrow-up1877
arrow-up1877imageSquiggly Boiemander.xyzfossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 2 months agomessage-square67fedilink
minus-squareBeeegScaaawyCripple@lemmy.worldlinkfedilinkEnglisharrow-up5·2 months agoohhhh so that’s The model for neural networks, not A model for neural networks
minus-squareTamo240@programming.devlinkfedilinkEnglisharrow-up8·2 months 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-22 months agoKinda but also no. That’s specifically a dense neural network or MLP. It gets a lot more complicated than that in some cases.
minus-squareNotANumber@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up5·2 months agoIt’s only one type of neural network. A dense MLP. You have sparse neural networks, recurrent neural networks, convolutional neural networks and more!
ohhhh so that’s The model for neural networks, not A model for neural networks
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.
It’s only one type of neural network. A dense MLP. You have sparse neural networks, recurrent neural networks, convolutional neural networks and more!