Learning rate schedules are important for making deep learning algorithms work better. They help change the learning rate during training. This way, the model can learn more effectively.
Step Decay: This method lowers the learning rate by a certain amount after a set number of training rounds.
Exponential Decay: In this approach, the learning rate gets smaller quickly over time.
Cyclic Learning Rate: This method changes the learning rate back and forth between a low and high value. This helps the model explore different options.
For example, a learning rate schedule might begin with a learning rate of 0.1. Then, it can cut that rate in half every 10 training rounds. This helps the model perform better and learn more steadily.
Learning rate schedules are important for making deep learning algorithms work better. They help change the learning rate during training. This way, the model can learn more effectively.
Step Decay: This method lowers the learning rate by a certain amount after a set number of training rounds.
Exponential Decay: In this approach, the learning rate gets smaller quickly over time.
Cyclic Learning Rate: This method changes the learning rate back and forth between a low and high value. This helps the model explore different options.
For example, a learning rate schedule might begin with a learning rate of 0.1. Then, it can cut that rate in half every 10 training rounds. This helps the model perform better and learn more steadily.