New answers tagged

1

You are correct. The main conceptual difference is that optimization is about finding the set of parameters/weights that maximizes/minimizes some objective function (which can also include a regularization term), while regularization is about limiting the values that your parameters can take during the optimization/learning/training, so optimization with ...


0

I don't know what is your dataset exactly look like. But based on assumption, I would like to refer something -- You can think your MDP environment this way action = {stay, go} reward = {something like based on visitor's satisfaction maybe rating} state = {current money in hand, city, other some variable those key feature to make next iteration action} I ...


0

People usually say that genetic algorithms are used to solve optimization problems, but when it comes to optimizing a specific function given in an analytic form (i.e. when it comes to finding a maximum or minimum of such a function), it may not be clear how to proceed. I have created a complete but simple implementation and explanation of how to solve this ...


Top 50 recent answers are included