Comment on Squiggly Boie

• HeyThisIsntTheYMCA@lemmy.world ⁨2⁩ ⁨days⁩ ago

  ohhhh so that’s The model for neural networks, not A model for neural networks

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  • Tamo240@programming.dev ⁨1⁩ ⁨day⁩ ago

    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.

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    • NotANumber@lemmy.dbzer0.com ⁨1⁩ ⁨day⁩ ago

      Kinda but also know. That’s specifically a dense neural network or MLP. It gets a lot more complicated than that in some cases.

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  • NotANumber@lemmy.dbzer0.com ⁨1⁩ ⁨day⁩ ago

    It’s only one type of neural network. A dense MLP. You have sparse neural networks, recurrent neural networks, convolutional neural networks and more!

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• Aceticon@lemmy.dbzer0.com ⁨1⁩ ⁨day⁩ ago

  I haven’t really done Neural Networks in 2 decades, and was under the impression that NNs pretty much dominate Machine Learning nowadays, whilst stuff like Genetic Algorithms were way less popular or not at all used anymore.

  Is that the case?

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  • howrar@lemmy.ca ⁨1⁩ ⁨day⁩ ago

    Neural networks are a class of models. Genetic algorithms are a class of learning algorithms. You use learning algorithms to train models. Genetic algorithms are a valid way of training neural networks, but this is not currently in vogue. They’re typically trained via gradient descent.

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  • Nikls94@lemmy.world ⁨1⁩ ⁨day⁩ ago

    I don’t know if it is the case for the world today, but all those Models behave like genetic algorithms and IF-functions with a little RNG sprinkled on top of them.

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    • Aceticon@lemmy.dbzer0.com ⁨1⁩ ⁨day⁩ ago

      You mean that they’re actually competing multiple variants of a model against each other to see which ones get closer to generating the expected results, and picking the best ones to create the next generation?

      Because that’s how Genetic Algorithms work and get trained, which is completelly different from how Neural Networks work and get trained.

      Also the links in Neural Networks don’t at all use IF-functions: the output of a neuron is just a mathematical operation on the values of all it’s inputs (basically a sum of the results of a function applied to the input numbers, though nowadays there are also cyclic elements).

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