475.475. Elastic Weight Consolidation

Elastic Weight Consolidation

**Problem:** Implement the Elastic Weight Consolidation (EWC) algorithm to update the weights of a neural network when it is trained on multiple tasks.

**Example:** Suppose we have a neural network that is trained on a sequence of tasks. Each task consists of a set of input-output pairs. The network is trained on the first task, then on the second task, and so on. However, after training on the second task, the network has forgotten how to perform the first task. To prevent this, we can use the EWC algorithm to update the weights of the network. The EWC algorithm adds a penalty term to the loss function to prevent the network from forgetting the first task.

**Constraints:** The EWC algorithm should be implemented to update the weights of the network when it is trained on multiple tasks.