Debugging deep learning models presents unique challenges compared to traditional software debugging, due to the complexity and adaptability of neural networks. While software debugging typically involves finding and fixing deterministic errors in code, deep learning models can exhibit issues even when the implementation is correct, such as when trained on improperly preprocessed data. These models are powerful enough to adapt to incorrect inputs, which can lead to failures when confronted with properly processed data. The article emphasizes the importance of a strategic approach to debugging, which includes checking model implementation, verifying input data, initializing parameters correctly, starting with simple models as baselines, and monitoring intermediate outputs. Techniques like feature normalization, preventing vanishing gradients, and using regularization methods like dropout and early stopping are crucial to prevent overfitting. Additionally, documenting and tracking experiments with tools like Neptune can aid in reproducing and diagnosing issues. The process demands patience and a deep understanding of machine learning principles, as models may take unexpected amounts of time to reach optimal solutions.