A loss function turns how wrong a model is into a single number, which training then works to make smaller.
The loss function defines what wrong means. The learning rate decides how big a correction to make. Direction and distance.
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A loss function measures how wrong a model's predictions are and turns that into one number. Training is the process of making that number smaller.
It is the only thing the model is actually optimising. Whatever you choose to measure is what it will get good at, so picking the wrong loss quietly teaches the wrong behaviour.
Think of it like this. Think of a score in golf. Lower is better, and every shot changes it. The player never sees the whole course at once, only whether the last decision moved the number in the right direction.
