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.
In the above image, we can clearly see that a big learning rate leads us to move away from the desired output. However, having a small learning rate leads us to the desired output eventually.
Posted Date:- 2022-02-15 11:07:36
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