Phase 4 · Deep learning · Tooling
Capstone: train a classifier
Build, train and debug a network end to end.
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Why it matters
This capstone puts Phase 4 together on a problem you already know: predicting churn from Phase 3. The point isn't only to train a network; it's to answer the question every team faces: is the neural network actually better than the simple model? Learning to answer that rigorously, including when the answer is "no", is what separates engineering from enthusiasm.
What you'll be able to do
- Prepare tabular data for a neural network without leaking test information
- Run the sanity checks before trusting a training run
- Train a regularized network with early stopping on a validation metric
- Compare it honestly with a classical baseline, and know when deep learning pays off