Explain how you would adapt the general principles of gradient-based optimization to address the challenges in training this particular neural network. Be sure to discuss potential pitfalls and tradeoffs.

I would focus on avoiding local minima by using techniques such as regularization, early stopping, or more advanced optimization algorithms like Adam. I’d also explore strategies for initializing parameters effectively, considering methods like Xavier initialization or Kaiming initialization. Moreover, I’d employ techniques to monitor and adapt the learning rate during training.