👉 Loss computing is a fundamental concept in machine learning that quantifies the discrepancy between predicted outcomes and actual values, serving as a measure of how well a model is performing. It is typically expressed as a scalar value that represents the average error or deviation of predictions from the true labels, often calculated using specific loss functions tailored to the problem type (e.g., mean squared error for regression, cross-entropy loss for classification). By minimizing this loss during training, models learn to make more accurate predictions. The choice of loss function is crucial as it guides the optimization process, influencing the model's ability to generalize from training data to unseen examples. Effective loss computing is essential for evaluating model performance and guiding the tuning of hyperparameters to achieve optimal results.
