Can two identical networks learn different behavior?
Yes. Different random seeds, training examples, data order, and optimization settings can lead networks with the same architecture to different final parameter values.
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More FAQs in How does an untrained neural network differ from a trained one?
No. Many weights use random initialization, but biases, normalization parameters, or other components may use fixed starting values chosen for the architecture.
No. A pre-trained model already contains learned patterns. It may still need prompting, adaptation, or additional training to perform a new task reliably.
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