What are the benefits of using deep learning over traditional machine learning methods?
Instruction: List and briefly explain the advantages of deep learning compared to traditional machine learning approaches.
Updated
Example Answer
Deep learning is especially useful when learning representations from images, audio, text, or other complex inputs. A pretrained network can provide features that would be difficult to engineer manually, and I can fine-tune it for a related task.
That is an advantage to test, not a guarantee that deep learning wins. On structured tabular data, I would include a strong tree-based baseline. With limited labels, a simpler model or a frozen pretrained encoder may be cheaper and more reliable than training a large network from scratch. More data helps when it adds relevant coverage; noisy labels, duplicates, or a mismatched distribution can still hurt performance.
My comparison would use the same deployment-relevant split and measure both predictive quality and operating cost. If a larger network gives a small improvement but exceeds the latency or memory budget, I would prefer the simpler model. I would also check whether pretraining data and the new task are sufficiently related to avoid negative transfer.
Follow-up
For a new customer-churn table, start with a regularized linear model and boosted trees alongside any neural baseline. For defect images, also evaluate a pretrained image encoder.