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.