Is backpropagation the same as gradient descent?
No. Backpropagation computes gradients efficiently. Gradient descent is an optimization rule that uses those gradients to choose new parameter values during training.
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More FAQs in What is backpropagation, and why is it necessary?
No. It propagates derivative information through the computation graph. That information describes how changes in intermediate quantities and parameters affect the loss.
Ordinary response generation uses forward computations with fixed learned parameters. Backpropagation is normally reserved for training or other explicit procedures that need gradients.
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