The answer
Yes. Different random seeds, training examples, data order, and optimization settings can lead networks with the same architecture to different final parameter values.
How does an untrained neural network differ from a trained one?
Can two identical networks learn different behavior?
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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