Let’s say you have an article that talks about cats and dogs (because who doesn’t love those furry little creatures?). And let’s also assume that we want to find out which parts of this article specifically discuss the topic of “cats” versus “dogs”. Well, that’s where our fancy math friend comes in.

First, we need to identify all the instances of the words “cat” and “dog” within the text (which is called tokenization). Then, for each occurrence of these words, we calculate a score based on how likely it is that they are actually part of a larger sentence or paragraph about cats/dogs. This score takes into account factors like the position of the word in relation to other words, as well as any contextual clues (like whether there’s an article “the” before the noun).

Once we have these scores for all the tokens, we can then use them to identify which parts of the text are most likely to be about cats/dogs. And that’s where our fancy math friend comes in again by calculating a loss function (which is basically just a way of measuring how far away each token is from its true position within the larger piece of text).

So, for example, if we have a sentence like “I love my cat and my dog”, then our loss function might look something like this:
– For the word “cat” (which has a high score because it’s part of a larger sentence about cats), the loss would be very small.
– For the word “dog” (which also has a high score, but is located later in the sentence), the loss might be slightly higher due to its position relative to other words.
– And for any random words that don’t have anything to do with cats/dogs (like “I”, “love”, or “my”), the loss would be very large because they are not part of a larger sentence about these topics.

By minimizing this loss function, we can essentially train our fancy math friend to better identify which parts of the text are most likely to be about cats/dogs (or whatever other topic you’re interested in). And that’s pretty cool if you ask me!