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What are the key takeaways to troubleshoot deep neural networks?
Conclusion - Troubleshooting


  • 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.