What do you understand by learning rate in a neural network model? What happens if the learning rate is too high or too low?

The learning rate is one of the main configurable hyperparameters used in training neural networks. Choosing a learning rate is one of the most difficult aspects of training neural networks. A huge learning rate value means that the model needs few training eras and changes fastly. On the other hand, a small learning rate means that the model may take a long time to converge or may not converge and get stuck at a sub-optimal solution. Therefore, it is recommended to find a good learning rate value by trial and error rather than using a learning rate that is too low or too high.
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