Controlling the Learning Rate in Machine Learning
When studying, going too quickly through material can lead to misunderstandings and mistakes.
On the other hand, if you study too slowly, you may finish learning after the exam has already taken place.
The same principle applies to machine learning models.
The hyperparameter that determines how quickly a model learns is the Learning Rate.
The learning rate determines how rapidly the model adjusts the weights.
Why the Learning Rate is Important
During the training process, a model needs to find the optimal weights.
If the learning rate is too high, the model might overshoot and never settle on the optimal value. If it’s too low, learning could become so slow that it stalls before reaching a good solution. To simplify, imagine descending from a mountain peak into a valley.
If the Learning Rate is Too High
Taking large steps might cause you to overshoot the valley, continually going back and forth without finding the optimal value.
If the Learning Rate is Too Low
Taking tiny steps will significantly prolong your journey, and you might stop prematurely.
By setting an appropriate learning rate, a model can learn quickly and effectively reach the optimal value.
How to Set the Learning Rate
The learning rate is set between 0 and 1. It's wise to start with a smaller value rather than too large, then adjust incrementally.
Typically, starting between 0.001 and 0.01 is common, but finding the most suitable learning rate often requires several experimental iterations.
In the next lesson, we will learn about the batch size, which determines how much data is used in a single learning iteration.
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