[Verse 1] Start with a perceptron, just one simple node Takes inputs, weights them down, finds the code Add more layers deep, now we're building networks wide Activation functions help the signals come alive ReLU clips the negatives, keeps the positive flow GELU and SiLU smooth the way the gradients go [Chorus] Build and train and debug well PyTorch makes the magic swell Backprop flows through every layer Gradients are computation's prayer SGD and Adam too AdamW will see you through Neural networks learn the way When you code them right today [Verse 2] Forward pass computes the graph, automatic differentiation Backward pass finds every grad through chain rule calculation Loss curves tell the story of how your model learns Watch for overfitting when the validation burns Dropout randomly zeros out, keeps the model lean Batch norm standardizes what the layers have seen [Chorus] Build and train and debug well PyTorch makes the magic swell Backprop flows through every layer Gradients are computation's prayer SGD and Adam too AdamW will see you through Neural networks learn the way When you code them right today [Bridge] Xavier starts the weights just right for tanh and sigmoid He initialization works when ReLU's applied Learning rate scheduling helps you find the perfect pace Mixed precision training saves both memory and space Gradient accumulation when your batches can't be large Monitor the gradients, see who's taking charge [Verse 3] Layer norm across features, weight decay fights the bloat Fashion-MNIST waits for you to crack its clothing code Multi-layer networks stack like building blocks so tall Computational graphs connect and link them all From tensor to tensor, watch the data flow PyTorch autograd helps your neural network grow [Chorus] Build and train and debug well PyTorch makes the magic swell Backprop flows through every layer Gradients are computation's prayer SGD and Adam too AdamW will see you through Neural networks learn the way When you code them right today [Outro] LSUV fine-tunes the start Learning rate finder's an art Neural networks, fundamental Make AI dreams substantial
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