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.