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Loss Function

Funzione di perdita

The loss function is the numerical measure of how far a model's predictions stray from the correct answers. It returns a high value when the model is very wrong and a low one when it's close: it's the compass guiding all learning. Think of a teacher assigning an error score to each assignment: the student-model's goal is to lower that score lesson after lesson. Different forms suit different tasks: mean squared error for predicting numbers, cross-entropy for classification and next-token prediction. The loss value is then differentiated by backpropagation to figure out how to adjust parameters.

Definition

Choosing the loss function defines what 'doing well' actually means for the model. A poorly designed loss can push training toward unwanted behavior, while a well-calibrated one aligns optimization with the real goal.

Related terms

• Backpropagation
• Gradient Descent
• Logits
• Softmax
• Machine Learning

More in Fondamenti AI

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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