Answer: a. "locally optimal solutions" rather than "globally optimal solutions"
Explanation:
Neural networks can sometimes get stuck in locally optimal solutions, where the network finds a solution that is optimal within a small region of the solution space but not necessarily the best possible solution globally. This can be a weakness because the network may not find the globally optimal solution for more complex problems.
Option b is incorrect because neural networks can model a wide range of variables and non-linear functions. This makes them quite versatile.
Option c is incorrect because while neural networks can have complex structures that make interpretation challenging, techniques like feature importance and visualization can help improve interpretability.
