Loss Optimization

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Loss optimization is the part of training where a model learns by reducing its mistakes. It starts with a loss function, which gives a number that tells the model how wrong its current prediction is. Training then tries to push that number down by adjusting the model’s internal parameters step by step. For example, if a model predicts a house price that is far from the real price, the loss will be high. Optimization updates the model, so the next prediction is closer.

This cycle repeats many times as the model sees more data. The goal is to improve in a way that still works on new data. Teams usually watch the loss on both the training data and a separate validation set. If training loss keeps dropping while validation loss stops improving, it can signal overfitting, meaning the model is starting to memorize instead of learning general patterns.

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