Choosing the right architecture is vital. Share how you determine the most suitable deep learning model for your specific tasks.
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Overfitting can hinder model performance. Discuss techniques like regularization, dropout, or data augmentation that have helped you address overfitting.
I’ve been running into unexpected gradients or zero gradients in some layers, and it’s been tricky to trace where things go wrong. Any tips, tools, or workflows that help identify and fix these problems would be really appreciated!