I have been really impressed by deep learning in medical imaging catching patterns humans might miss is a game-changer.

But also seen it struggle in messy, real-world business problems where data is noisy or limited.

Sometimes simpler, more interpretable models just work better.

Deep learning has certainly shown impressive results in areas like computer vision, NLP, and autonomous systems, but it’s not always a one-size-fits-all solution.

In practice, a lot of the challenges come from data itself cleaning large datasets, dealing with missing or biased information, and making sure the data truly represents the problem at hand.

Computational resources and model interpretability are other hurdles that can make implementation tricky, especially for complex real-world scenarios.

Some applications have been surprisingly successful like image recognition in medical diagnostics or language models assisting with text analysis where the models uncover patterns that were hard to detect manually.

At the same time, there have been cases where deep learning didn’t live up to expectations, often because the problem wasn’t well-suited for neural networks or the data wasn’t sufficient.

The key seems to be understanding both the capabilities and the limitations before diving in.