Training loss is a measure used in machine learning to quantify the error between the predicted outputs of a model and the actual outcomes during the training phase. It helps in adjusting the model's weights through optimization algorithms like gradient descent.
Understanding Training Loss
In machine learning, models learn by minimizing this loss. Training loss is calculated using a loss function, chosen based on the specific task, such as mean squared error for regression tasks. The decrease in training loss over epochs indicates learning, whereas if the loss stops decreasing, it may imply that the model has learned as much as it can from the training data.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. Come join us and 1000+ others in a chat that’s thoroughly SFW.