In the context of deep learning, the primary objective is rarely to recover the exact true underlying parameters of the data generation process. Instead, the focus is on discovering parameter values that result in highly accurate predictions. Even though optimization landscapes for deep networks are complex, algorithms like stochastic gradient descent perform exceptionally well. This practical success is largely due to the fact that deep models possess numerous different parameter configurations that all lead to excellent predictive performance.