loss functions
Loss functions are mathematical measures that quantify the error between predicted values and actual values in machine learning models. They help optimize the model's performance by guiding the learning process towards minimizing the difference between predicted and target values.
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Related Concepts (22)
- average precision loss
- backpropagation
- binary cross-entropy
- categorical cross-entropy
- chi-square loss
- contrastive loss
- dice loss
- exponential loss
- focal loss
- fully connected layers
- generalized adversarial loss
- hinge loss
- huber loss
- kullback-leibler divergence
- log loss
- mean absolute error (mae)
- mean squared error (mse)
- quantile loss
- rmsprop loss
- softmax loss
- triplet loss
- wasserstein loss