14 key clues or concerns commonly associated with deep learning:

 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].

6. *Hyperparameter Tuning*: Finding the optimal settings for a model’s hyperparameters is complex and time-consuming[1].

7. *Scalability*: Scaling models to handle larger datasets or more complex tasks can be challenging and resource-intensive[1].

8. *Ethical and Bias Issues*: Models can perpetuate or amplify biases present in training data, leading to unfair or discriminatory outcomes[1][2][3][5][6].

9. *Data Privacy and Security*: The need for large datasets raises concerns about privacy, consent, and the risk of data breaches[2][3][5].

10. *Hardware Limitations*: Not all organizations have access to the necessary hardware, creating barriers to entry and innovation[1][5][6].

11. *Adversarial Attacks*: Deep learning models can be vulnerable to subtle, malicious input manipulations that cause incorrect predictions[1].

12. *Generalization Challenges*: Models may struggle to apply learned knowledge to new domains or environments, limiting adaptability[6].

13. *Domain Expertise Requirement*: Effective deep learning applications often require significant domain knowledge to frame problems and interpret results[5].

14. *Environmental Impact*: High computational demands lead to increased energy consumption, raising concerns about the environmental footprint of large-scale deep learning systems[6].

These concerns highlight both the technical and ethical complexities involved in developing and deploying deep learning solutions. Addressing them is crucial for responsible and effective use of this technology.

[1] https://www.geeksforgeeks.org/deep-learning/challenges-in-deep-learning/

[2] https://codefinity.com/blog/Ethical-Considerations-in-Deep-Learning

[3] https://milvus.io/ai-quick-reference/what-are-the-ethical-concerns-of-deep-learning-applications

[4] https://www.sciencedirect.com/science/article/pii/S2772941923000030

[5] https://www.geeksforgeeks.org/deep-learning/advantages-and-disadvantages-of-deep-learning/

[6] https://yetiai.com/what-is-wrong-with-deep-learning/

[7] https://www.analyticsinsight.net/deep-learning/understanding-the-limitations-of-deep-learning-models

[8] https://iq.opengenus.org/problems-in-deep-learning/

[9] https://milvus.io/ai-quick-reference/what-are-common-challenges-in-deep-learning-projects

[10] https://www.linkedin.com/pulse/deep-learning-question-answers-rashmi-priya-3p8pc

[11] https://www.analyticsinsight.net/deep-learning/challenges-and-limitations-of-deep-learning-what-lies-ahead

[12] https://www.zendesk.com/blog/machine-learning-and-deep-learning/

[13] https://www.scribd.com/document/848385838/Perspectives-and-Issues-in-Deep-Learning

[14] https://www.scribd.com/document/805629346/Deep-Learning-AD3501-Important-Question-and-2-Marks-With-Answers-Unit-1

[15] https://www.e-spincorp.com/deeplearning/

[16] https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm

[17] https://www.xenonstack.com/insights/deep-learning-applications

[18] https://www.datasciencecentral.com/things-to-watch-out-for-when-using-deep-learning/

[19] https://arxiv.org/pdf/2309.03774.pdf

[20] https://www.esann.org/sites/default/files/proceedings/legacy/es2016-23.pdf