Read training signals
While a model trains, you watch two numbers. The training loss says how well it fits the examples it learns from. The validation loss says how well it does on examples it has never seen. The first almost always goes down. The second is the one that matters, because a model can memorise its training examples and still be useless on anything new. When training loss falls while validation loss rises, that is what is happening.
About this lesson
Words to know
- validation set
- Examples kept out of training and used only to check how the model does on data it has not seen.
- overfitting
- When a model memorises its training examples and gets worse on new ones.
- loss
- One number that says how wrong the model is. Training tries to make it smaller.
Why it matters
The tiny GPT in the workshop prints both losses while it trains. Reading them is how you decide when to stop, and whether a change to the model helped.
With a year
How it works, a worked example, the exercise with its tests, three hints and the explained solution are in the app, with a year of Algo150 for $19. The first chapter, Math foundations, is free for everyone: start with Follow a gradient.