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Training loss: Difference between revisions
Created page with "{{see also|Machine learning terms}} ==Introduction== Training loss is an important metric in machine learning that measures the discrepancy between predicted output and actual output. It helps evaluate a model's performance during training, with the aim being to minimize this loss so that it can generalize well on unseen data. ==Types of Loss Functions== Machine learning employs a variety of loss functions, depending on the problem being solved and the model being emplo..."
(Created page with "{{see also|Machine learning terms}} ==Introduction== Training loss is an important metric in machine learning that measures the discrepancy between predicted output and actual output. It helps evaluate a model's performance during training, with the aim being to minimize this loss so that it can generalize well on unseen data. ==Types of Loss Functions== Machine learning employs a variety of loss functions, depending on the problem being solved and the model being emplo...") |
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