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What is Decreasing Loss with Stagnant Accuracy Issues about?

The document discusses the implications of decreasing loss during model training while accuracy remains stagnant or declines, suggesting potential issues like class imbalance, overfitting, or inappropriate loss functions. It also lists common activation functions with their ranges and best use cases, emphasizing that Sigmoid is ideal for binary classification output layers while ReLU is preferred for hidden layers. Additionally, it outlines various loss functions suitable for different tasks, such as Mean S

Author

aidanmsn2

Language

EN