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Deep learning is powerful, but it is not a magic hammer for every problem. Knowing when to reach for it is a real skill. 🎯

Deep nets dominate vision: classifying photos, detecting objects, and reading handwriting. Raw pixels are exactly the messy data they thrive on.

For language, deep learning powers translation, chatbots, and search. Text is full of subtle patterns that learned features capture beautifully.

Speech recognition and music tagging lean on deep nets too. Rich audio signals are another sweet spot where learned features really pay off.

Notice the pattern: deep learning wins on high-dimensional, unstructured data like images, sound, and text, where rules are too messy to write by hand.

With only a few hundred rows, a deep net often overfits, memorizing noise instead of learning. Hungry models need lots of examples to behave.

On clean spreadsheet-style tabular data, gradient-boosted trees often match or beat deep nets while training far faster and cheaper.

If you must explain every decision, deep nets are tricky. Their inner logic is hard to read, so a simpler, interpretable model may be safer.

Deep models can be slow and pricey to train and run. That cost in compute and energy matters when a lean model would do the job.

A wise habit: try a simple baseline first. If logistic regression already nails it, you may not need a deep net at all.

The real lesson is fit: reach for deep learning on rich, plentiful data, and pick simpler models when data is small, tabular, or must be explained.

In which situation is deep learning usually the weaker choice?

You learned the fit: deep learning wins on rich vision, language, and audio data, but simpler models lead on small, tabular, or must-explain problems. ⚖️