usually when we train deep models we are minimizing a cost function and so we're using an optimization algorithm to perform minimization there are a lot of things that can go wrong with minimization especially when you're training a deep model you can approach a saddle point rather than approaching a minimum you can approach a local minimum rather than a global minimum we're starting to become skeptical that local minima are as much of a problem as we used to think they were and you can have all kinds of things we think they're