Model parameters & Hyper parameters of Neural Network & their tuning in training & validation stage
The parameters of a neural network are typically the weights of the connections. In this case, these parameters are learned during the training stage. So, the algorithm itself (and the input data) tunes these parameters. 

The hyper parameters are typically the learning rate, the batch size or the number of epochs. The are so called "hyper" because they influence how your parameters will be learned. You optimize these hyper parameters as you want (depends on your possibilities): grid search, random search, by hand, using visualisations... The validation stage help you to both know if your parameters have been learned enough and know if your hyper parameters are good. 

If you want to know more about hyper parameters and parameters in general in machine learning, look for "deep learning versus shallow learning".