Test task: MNIST, Max Pooling CNN with ReLU

1. Set the initial learning rate to a low or medium value.
2. Set the momentum to a high or medium value.
3. Set the power to a low or medium value.
4. Set the lambda to a low or high value.
5. Consider the characteristics of the task, such as the dataset

size, model architecture, and the complexity of the prediction task,
when adjusting the parameters.

6. For tasks with larger datasets, a higher initial learning rate and

lower momentum may be more suitable.

7. For tasks with smaller datasets, a lower initial learning rate and

higher momentum may be more suitable.

8. For tasks with more complex models, a higher initial learning rate

and lower momentum may be more suitable.

9. For tasks with simpler models, a lower initial learning rate and

higher momentum may be more suitable.

10. For tasks with more complex prediction tasks, a higher initial

learning rate and lower momentum may be more suitable.
MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks