What is Transfer Learning?

Transfer learning is a technique where a neural network trained for one task is used for another. It's like reusing a old pair of socks for a new pair of shoes.

This can be useful when you have a large model trained on one task and want to adapt it for another task that's similar but not identical.

For example, a model trained to recognize cats can be used to recognize dogs with some extra training.

But beware, transfer learning can also lead to weird, unexplainable results.

See examples of transfer learning gone wrong

Why use Transfer Learning?

It's faster than training a new model from scratch, like using a pre-made pizza crust instead of making your own dough from scratch.

It can be more effective, especially when the new task is a variation of the old task.

But be careful not to overdo it, like using the same old crust for too many pizzas.

See use cases for transfer learning