Artificial Intelligence Programming Practice Exam

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What is gradient descent used for in machine learning?

To increase the complexity of the model

To minimize the loss function through iterative adjustments

Gradient descent is a key optimization algorithm used in machine learning to minimize the loss function. The loss function quantifies how well a model's predictions align with the actual outcomes; minimizing this function is crucial for improving model performance.

In gradient descent, the algorithm iteratively adjusts the model's parameters by calculating the gradient (or slope) of the loss function with respect to the parameters. This involves taking small steps in the direction that reduces the loss, which helps the model learn from the data it is trained on. Over successive iterations, these adjustments refine the model's predictions, making it more accurate.

This process is fundamental to training many types of machine learning models, particularly neural networks, where the complexity of the model and the high dimensionality of the parameter space make gradient descent an effective method for optimization.

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To evaluate model performance

To organize data into datasets

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