Learning rate is one of the most important configurable hyperparameters used in the training of a neural network. The value of the learning rate lies between 0 and 1. Choosing the learning rate is one of the most challenging aspects of training a neural network because it is the parameter that controls how quickly or slowly a neural network model adapts to a given problem and learns. A higher learning rate value means that the model requires few training epochs and results in rapid changes while a smaller learning rate implies that the model will take a long time to converge or might never converge and get stuck on a suboptimal solution. Thus, it is advisable not to use a learning rate that is too low or too high but instead a good learning rate value should be discovered through trial and error.