What BinRoo called "parameters" are usually known as "hyperparameters" (parameters are the settings that are learned through gradient descent). There are methods to learn hyperparameters automatically [1], but they're extremely computationally intensive. Even with a single set of hyperparameters, it can take hours to days for training to converge. Now imagine having to run hundreds of experiments to find the best setting.

[1] https://papers.nips.cc/paper/4443-algorithms-for-hyper-param...

> Why can't tweaking parameters be automated?

Not only that it can be done, but it works even by random trial. Sophisticated optimization techniques work out just 2x faster, so if you have cheap GPU, you can run 2x more trials and get your hyperparameters fine tuned.

If you want to apply optimization, you can use one of the popular libraries like hyperopt and MOE.

Good question! Brute-force searching to find values of a parameter is a start. But, you see the issue here, right? It'll take a longass™ time. Lucky for us, learning the weight parameters of a neural network can be accomplished using back-propagation (or other methods) to speed up this search, and we do not need to brute force all possibilities. That same technique is not always applicable for hyper-parameters.

> I know nothing about deep learning but I'm curious. Why can't tweaking parameters be automated? Why is human intuition necessary here?

ok, well, imagine (for simplicity) that you are standing on a 3d-surface with mountains and valleys and such. you can only see so far. you need to walk/run etc. to get to a pot of gold which is there at the global minima of this landscape.

what do you do ? one option is choose gradient descent :) start walking somewhere, and see if the your height, from your previous position, is decreasing. if it is, then maybe you are on the right track...