The answer
Parameters are learned values reused across inputs. Activations are intermediate results of processing a particular input and can change even when parameters stay fixed.
What is a forward pass in neural-network training?
What is the difference between parameters and activations?
More FAQs in What is a forward pass in neural-network training?
A normal forward pass calculates outputs using current weights. Learned parameter updates happen through the optimizer, although some training-mode layers may update separate running statistics.
Training commonly keeps activations, gradients, and optimizer state in addition to model parameters. Inference usually avoids much of that extra storage and computation.
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