[Verse 1] Your neural network starts its quest to learn Each prediction tested, tables turn The loss function measures where you missed the mark Mean squared error lights up in the dark Cross entropy whispers what went wrong Teaching algorithms to sing their song [Chorus] Loss goes down, gradients guide the way Splitting data keeps the bias at bay Training, testing, validation too Seventy, twenty, ten will see you through Don't let overfitting steal your thunder Regularization pulls you back fromunder [Verse 2] Gradient descent climbs the mountain backwards Tiny steps where mathematics matters Learning rate controls your wandering pace Too fast you'll bounce all over the place Too slow you'll crawl like molasses thick Momentum helps your convergence stick [Chorus] Loss goes down, gradients guide the way Splitting data keeps the bias at bay Training, testing, validation too Seventy, twenty, ten will see you through Don't let overfitting steal your thunder Regularization pulls you back from under [Bridge] When your model memorizes every detail That's overfitting and you're bound to fail Early stopping when validation peaks Dropout neurons playing hide and seek L1 and L2 penalties apply Keep your weights from flying too high [Verse 3] Cross validation folds your data neat K times around makes training complete Bias variance tradeoff in your hands Underfitting means your model barely stands Sweet spot balance is what you seek Where generalization makes you unique [Chorus] Loss goes down, gradients guide the way Splitting data keeps the bias at bay Training, testing, validation too Seventy, twenty, ten will see you through Don't let overfitting steal your thunder Regularization pulls you back from under [Outro] From random weights to patterns learned Each epoch shows what knowledge earned The model trained will serve you well When new data has its tale to tell
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