Learning rate is a number that ranges from 0 to 1. It is one of the most important tunable hyperparameters in neural network training models. The learning rate determines how quickly or slowly a neural network model adapts to a given situation and learns. A higher learning rate value indicates that the model only needs a few training epochs and produces rapid changes, whereas a lower learning rate indicates that the model may take a long time to converge or may never converge and become stuck on a poor solution. As a result, it is recommended that a good learning rate value be established by trial and error rather than using a learning rate that is too low or too high.