Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

The primary function of an optimizer in machine learning is to update model parameters. In the context of training a model, the optimizer plays a crucial role in minimizing the loss function by adjusting the parameters (weights and biases) based on the gradients computed during the backpropagation process. By iteratively modifying the parameters in response to the loss, the optimizer helps the model converge to a state where its predictions are as accurate as possible.

During training, the optimizer determines the direction and magnitude of the updates needed to improve the model's performance based on the gradients derived from the loss function. This ongoing adjustment of parameters allows the model to better fit the training data, thereby enhancing its ability to generalize to unseen data during evaluation.

The other options pertain to different aspects of the machine learning process. Initializing model parameters is often done before training begins. Evaluating the performance of the model occurs after training, typically involving metrics calculated on a test dataset. Creating training datasets is part of the preprocessing and data preparation phase, which occurs prior to the actual training process and optimization.