# Conclusion {% embed url="" %} Conclusion - Troubleshooting {% endembed %} ## Summary * Deep learning debugging is hard due to many competing sources of error. * To train bug-free deep learning models, you need to treat building them as an iterative process. * Choose the simplest model and data possible. * Once the model runs, overfit a single batch and reproduce a known result. * Apply the bias-variance decomposition to decide what to do next. * Use coarse-to-fine random searches to tune the model’s hyper-parameters. * Make your model bigger if your model under-fits and add more data and/or regularization if your model over-fits. --- # 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://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/conclusion.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.