Set Up a Learning Rate in the Training Classifier

Fixing the problem

Unfortunately, we can’t completely rely on the very last training example. This is an important idea in machine learning. To improve the results, we moderate the updates. This means that instead of jumping enthusiastically to each new $A$, we take a fraction of the change $ΔA$. That way, we move in the direction that the training example suggests, but we do so cautiously and keep some of the previous value, which we potentially arrived at through many training iterations. We saw this idea of moderating our refinements before with the simpler miles to kilometers predictor, ...