What does an artificial neural network do after receiving poor predictions from its training data?

What does an artificial neural network do after receiving poor predictions from its training data?

• A. It collects new data
• B. It reinitializes with new random weights
• C. It adjusts the weights of the connections to improve accuracy
• D. It stops training

Answer: (C)

An artificial neural network is designed to learn from its training data by identifying patterns and making predictions. After making predictions that are deemed poor or incorrect, the network takes a corrective action by adjusting the weights of the connections between its neurons. This process is known as backpropagation. During backpropagation, the network calculates the error, which is the difference between the predicted output and the actual output. It then uses this error to adjust the weights in such a way that the network can improve its future predictions. The update of the weights is influenced by how much they contributed to the error, allowing the model to learn from its mistakes and progressively enhance its accuracy. The other options would not accurately describe the process that occurs after receiving poor predictions. Collecting new data may be useful for training, but it is not a direct response to poor predictions. Reinitializing with new random weights would essentially stop leveraging the knowledge gained through previous training, which is counterproductive. Stopping training would mean the network remains static and fails to improve, which contradicts the objective of refining its predictions.

An artificial neural network is designed to learn from its training data by identifying patterns and making predictions. After making predictions that are deemed poor or incorrect, the network takes a corrective action by adjusting the weights of the connections between its neurons. This process is known as backpropagation.

During backpropagation, the network calculates the error, which is the difference between the predicted output and the actual output. It then uses this error to adjust the weights in such a way that the network can improve its future predictions. The update of the weights is influenced by how much they contributed to the error, allowing the model to learn from its mistakes and progressively enhance its accuracy.

The other options would not accurately describe the process that occurs after receiving poor predictions. Collecting new data may be useful for training, but it is not a direct response to poor predictions. Reinitializing with new random weights would essentially stop leveraging the knowledge gained through previous training, which is counterproductive. Stopping training would mean the network remains static and fails to improve, which contradicts the objective of refining its predictions.