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How to Choose the Optimal Learning Rate for Neural Networks
Guidelines for tuning the most important neural network hyperparameter with examples
Hyperparameter tuning or optimization is a major challenge when using ML and DL algorithms. Hyperparameters control almost everything in these algorithms.
I’ve already discussed 12 types of neural network hyperparameters with a proper classification chart in my “Classification of Neural Network Hyperparameters” post.
Those hyperparameters decide the time and computational cost of running neural network models. They can even determine the network’s structure and finally, they directly affect the network's prediction accuracy and generalization capability.
Among those hyperparameters, the most important neural network hyperparameter is the learning rate which is denoted by alpha (α).
Beginners in DL always ask how important is the learning rate. I can answer the question as follows.
When training a neural network, if I’m allowed to tune only one hyperparameter, the one that I choose to tune without any hesitation is the learning rate.
