Here are 14 key clues or concerns commonly associated with deep learning:
1. *Overfitting*: Deep learning models can memorize training data rather than generalizing, leading to poor performance on new, unseen data[1][6].
2. *Underfitting*: Conversely, models may fail to capture underlying patterns if they are too simple or not trained sufficiently[1].
3. *Data Quality and Quantity*: Deep learning requires large, high-quality datasets; insufficient or poor-quality data can degrade model performance[1][6].
4. *Computational Resources*: Training and deploying deep learning models demand significant computational power, often requiring specialized hardware like GPUs or TPUs[1][5][6].
5. *Interpretability (Black Box Problem)*: Deep learning models are often opaque, making it difficult to understand or explain their decisions[1][5][6].