Learning Vector Quantisers (LVQ) Explained by a student

[@claudiadacapo]

I'm not kidding you should make more videos. There are no youtubers making educational content about this and your explanation was perfect for my level of understanding and background knowledge. Perfect. You are a great teacher, thank you!

[@EmmanuelPeter-y4d]

Nice presentation man.. Thanks for taking out time to make the video.

[@harbhajansingh9882]

Hats off nice presentation ❤

[@IamAmitBarman]

I need code for this

[@piotr780]

what is difference between LVQ and kmeans ?

[@patrick-zb6fx]

LVQ is supervised learning, it goals more like KNN algorithm, to predict new input into it's closest classification. meanwhile k-means is unsuperverised learning, it's usually used to cluster data or classified random data into classes that you defined, the number of cluster is called K... correct me if i'm wrong

[@parmisbathaeiyan9955]

actually, LVQ works supervised and therefore, is a classification algorithm. not a clustering one.

[@alessandrobiagiotti617]

Actually LVQ is unsupervised because the algorithm is given the points and it infers the classes

[@iMilchshake]

@@alessandrobiagiotti617 huh? So according to your logic training a mlp is unsupervised, because it infers the output value? No, we give the algorithm points AND their classes, which makes it a supervised approach. If you dont believe a random guy in the yt comments just check wikipedia real quick.

[@vijayrajejadhav3369]

@@iMilchshake i think you are right

[@jovanrumenic1607]

Lvq1 is unsupervised because we don’t have training data(inputs and outputs), we only have inputs and it learns from them

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cool video bro

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