In neural networks, which of the following is adjusted during training?

In neural networks, the weights of the connections between neurons are adjusted during training. This adjustment is a critical part of the learning process, as weights determine the strength and direction of the influence that one neuron has on another. By modifying the weights based on the errors made in predictions, the model learns to make more accurate predictions over time. This process typically involves techniques such as backpropagation, where gradients of a loss function are calculated and used to update the weights.

Adjusting the weights also allows the neural network model to optimize its performance in recognizing patterns in the provided data. This optimization continues through multiple training iterations, refining the weights gradually until the prediction accuracy aligns closely with the desired outcomes.

While biases, the model architecture, and activation functions play important roles in how a neural network operates, they are not typically the parameters that are adjusted during the training process in the same direct manner as the weights. Biases can also be adjusted, but the primary focus at training time is on weights as they directly affect the output predictions and learning dynamics.