> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/conclusion.md).

# Conclusion

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Conclusion - Troubleshooting
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## 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.