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Could you elaborate on the significance of training and loss graphs in a neural network? What are some commonly used loss functions in this context, and how do they affect the shape of the loss graph? In what ways do the training and loss graphs differ, and how do they contribute towards optimizing and enhancing model performance?
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General
Focus on how different loss functions impact training dynamics and how interpreting these graphs helps diagnose issues like overfitting or underfitting.
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