[Verse 1] Start with layers stacked in rows Forward flow is all it knows Input travels left to right Hidden layers shine their light Universal theorem states Any function it creates Given width and given time Neural networks climb and climb [Chorus] Feed it forward, push it back Gradients upon the track ReLU flows and GELU glows Swish it smooth where learning goes Xavier starts, He takes the lead LSUV plants the perfect seed Batch and layer, drop and stay Neural foundations light the way [Verse 2] Backprop starts from final cost Chain rule saves what might be lost Partial derivatives cascade Down each layer, debts are paid Delta flows from right to left No computation is bereft Weight updates with learning rate Gradient descent seals the fate [Chorus] Feed it forward, push it back Gradients upon the track ReLU flows and GELU glows Swish it smooth where learning goes Xavier starts, He takes the lead LSUV plants the perfect seed Batch and layer, drop and stay Neural foundations light the way [Bridge] When ReLU dies, gradients cease GELU brings dying neurons peace Swish combines the best of both Smooth and sharp, they keep their oath Initialize with proper care Xavier uniform and fair He normal for ReLU's might LSUV makes the variance right [Verse 3] Batch norm shifts the mean to zero Makes your gradients a hero Layer norm works sample-wise When your batch size multiplies Dropout kills some neurons dead Regularization in your head But be careful when to use Each technique has its own clues [Chorus] Feed it forward, push it back Gradients upon the track ReLU flows and GELU glows Swish it smooth where learning goes Xavier starts, He takes the lead LSUV plants the perfect seed Batch and layer, drop and stay Neural foundations light the way [Outro] From inputs through to final score These foundations are your core Master these and you'll see clear How deep learning persevere
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