The answer
No. Many weights use random initialization, but biases, normalization parameters, or other components may use fixed starting values chosen for the architecture.
How does an untrained neural network differ from a trained one?
Are all initial neural network parameters random?
More FAQs in How does an untrained neural network differ from a trained one?
Yes. Different random seeds, training examples, data order, and optimization settings can lead networks with the same architecture to different final parameter values.
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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