Hyperparameter
A setting you choose before training, as opposed to a parameter the model learns during it.
Learning rate, batch size, number of layers, dropout rate — these are hyperparameters. They are picked by the developer and tuned against validation data. The distinction from parameters is simple: you set hyperparameters, training sets parameters.
In practice: Running the same job 20 times with different learning rates to find the one that converges best.