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

The primary function of weights in a neural network is to determine the importance of each input feature. Weights are numerical values assigned to each input feature that influence how much impact that feature will have on the output of the neural network. During the training process, the neural network adjusts these weights based on the loss function's feedback to minimize prediction errors. This adjustment process allows the neural network to learn patterns from the training data, effectively prioritizing certain features that are more indicative of the target output.

This function is critical for enabling the network to differentiate between important and less important input features, thereby improving the model's predictive performance. As the model encounters diverse data during training, the weights are updated iteratively through optimization techniques such as gradient descent. This continuous adjustment reflects the relative contribution of each input feature to the predictions, allowing the model to learn complex relationships in the data.

The other options represent functions that are either unrelated to the core mechanics of how neural networks operate or are specific processes that do not involve weights directly. For example, normalization pertains to preparing the data before it is fed into the neural network, clustering is an unsupervised learning technique for grouping data, and data visualization pertains to representing data graphically rather than influencing model training or inference.