# Fastai Learning Having a low training loss but high validation loss does not necessarily mean that your model is already overfitting. This is a point of confusion for most of us when starting out and trying to understand these ideas. As mentioned in the book; when the validation set accuracy stops getting better, and instead goes in the opposite direction and gets worse, that is when you can start thinking that the model is starting to memorize the training set rather than generalize, and has started to overfit. Taken straight from the book, Chapter 1 : ![image](https://user-images.githubusercontent.com/24592806/175348582-34fd36be-ee9d-4df8-897c-2e8d11f33580.png) In other words, as you progress further on the training, once the validation loss starts getting consistently worse than what it was before, (and hence meaning that it’s not able to generalize as well to unseen data, while getting better on training data) then the model has started to overfit. Not to be taken as an exact example, but I’ve tried to quickly sketch out what this could mean in practice. ![image](https://user-images.githubusercontent.com/24592806/175348697-713c69ac-a0d9-4fa8-8de5-c39dc77b2c1d.png) and metrics sees no improvement Source: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://til.kurianbenoy.com/fastai-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.