You define your output layer and the desired number of classes when you add your DNN at the bottom of the network.
<h3>Transfer learning: What is it?</h3>
- A pre-trained model is used as the foundation for a new model in the machine learning technique known as transfer learning.
- Simply expressed, an optimization that enables quick progress when modeling the second task is applied to a model that was trained on the first, unrelated job.
<h3>What varieties of transfer learning are there?</h3>
There are three varieties of training transfer:
- Training improves performance in the desired career or function, which is a positive transfer.
- Negative Transfer: Training results in lower performance in the intended position or function.
- Zero Transfer: Training has no effect on performance in the intended job or role, either up or down.
<h3>
What is an example of transfer learning?</h3>
- For instance, if you trained a straightforward classifier to determine whether a picture has a backpack, you might use that knowledge to recognize other things, like sunglasses.
- With transfer learning, we essentially attempt to apply the knowledge we have gained to new situations in order to comprehend the concepts more fully.
learn more about transfer learning here
<u>https://brainly.in/question/15404224</u>
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Answer:
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Answer:
"We like to think that we are unique and special, we have a set of experiences which are exclusive to us. Yet we are very predictable. Even when we are irrational, we are predictably irrational. Irrespective of our experiences and unique histories our brains respond to triggers in a predictable way."
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Answer:
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