I'm learning Neural Networks, and everything works as planned but, like humans do, adjusting themselves to learn more efficiently, I'm trying to understand conceptually how one might implement an auto adjusting learning rate for a Neural Network.I have tried to make it based on error, something like how bigger is error learning rate is getting bigger as well. [Could use some clarification here--not entirely sure what you're saying.  If can clarify, I'm happy to clean up the English. -DukeZhou]*If you want give me an example give it on a C based language or math because I don't have experience with Python or Pascal. 
How to implement an Automatic Learning Rate for a Neural Network?
neural networks
Adjusting for learning rate is a common scenario in machine learning. There is rich literature about it, countless papers. The most common implementation:AdaGradRMSPropAdamThere are many more variants. You'll need to do some research. Please take a look at:https://en.wikipedia.org/wiki/Stochastic_gradient_descent