L2 loss: Revision history

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18 March 2023

  • curprev 13:1213:12, 18 March 2023‎ Walle talk contribs‎ 2,352 bytes +2,352‎ Created page with "{{see also|Machine learning terms}} ==L2 Loss in Machine Learning== L2 Loss, also known as Euclidean Loss or Squared Error Loss, is a widely-used loss function in machine learning and deep learning. It is a popular choice for regression tasks, where the goal is to predict a continuous output value. L2 Loss quantifies the difference between the predicted output and the true output, providing a measure of model accuracy. ===Definition and Properties=== The L2 Loss is def..."
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