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What is an Objective Function in AI?

An Objective Function in AI, also known as a loss function or cost function, is a function that the model aims to minimize (or maximize) during training. It measures the difference between the model's predictions and the actual values, providing a measure of the model's performance.

How does an Objective Function work in AI?

An Objective Function works by quantifying the model's performance on the training data. During training, the model adjusts its parameters to minimize (or maximize) the objective function. This is typically done using an optimization algorithm, such as gradient descent, which iteratively adjusts the parameters in the direction that reduces the objective function.

The choice of objective function depends on the task. For example, for regression tasks, a common objective function is the mean squared error, which measures the average squared difference between the predicted and actual values.

What is the role of the Objective Function in AI?

The Objective Function plays a crucial role in AI. It guides the learning process by providing a measure of the model's performance that can be optimized. By minimizing (or maximizing) the objective function, the model can learn to make better predictions or decisions.

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