In this section, we'll discuss transfer learning and how we can use it to train our model easily and more efficiently. Transfer learning is the abstraction of knowledge from the original owner, in such a way that anyone can obtain it freely. In a way, it's similar to how humans transfer knowledge between generations, or to each other.
Originally, for humans, the only way to transfer experience was by talking. As you can imagine, this was crucial for our survival. Of course, we eventually found better ways of storing knowledge, that is, through writing. In this way, we preserved the knowledge in its original form for a longer period, making it more extendable. Even nowadays, the dissemination of information is a fundamental aspect of society. Almost every mathematical theory is built on top of existing ones, which were written years ago.
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