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Question & Answer
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Gradient descent minimizes cross-entropy by adjusting model parameters to increase the likelihood of the correct class predictions.
Gradient descent works by maximizing the cross-entropy loss, thus leading to poorer model performance.
The softmax function is unaffected by changes in model parameters during gradient descent.
Cross-entropy loss is only applicable for binary classification problems.
Understanding the Answer
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Gradient descent lowers the cross‑entropy loss by changing the model weights. Other options are incorrect because The idea that gradient descent increases the loss is a common mistake; Softmax does change when weights change.
Key Concepts
Multi-class Loss Functions
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Deep Dive: Multi-class Loss Functions
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Definition
Multi-class loss functions are designed to evaluate the performance of multi-class classification models by penalizing incorrect predictions. They include Neyman-Pearson loss, hinge loss, and logistic loss, each serving different optimization and evaluation purposes.
Topic Definition
Multi-class loss functions are designed to evaluate the performance of multi-class classification models by penalizing incorrect predictions. They include Neyman-Pearson loss, hinge loss, and logistic loss, each serving different optimization and evaluation purposes.
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