Lecture Note
University
Stanford UniversityCourse
CS229 | Machine LearningPages
2
Academic year
2023
anon
Views
46
What Learning Rate Should Your Machine LearningModel Use? The selection of learning rate is one of machine learning's most important andcontinually developing elements. To achieve the optimum results, it is critical tooptimize the learning rate, a hyperparameter that controls how rapidly amodel learns from its input. We will delve deeply into what the learning rate is,why it's crucial, and how to pick the best one for your machine learning modelin this article. What is the rate of learning? The step size at which a model learns from its data is determined by thelearning rate, which is a scalar variable. In layman's terms, it establishes, basedon the gradient of the loss function, how rapidly a model changes itsparameters. The model minimizes the cost function and enhances itspredictions by updating the parameters in the direction of the gradient, whichis the direction in which the cost function changes the greatest. Why Does the Learning Rate Matter So Much? Because it controls how quickly a model learns from its data, the learning rateis crucial. The model will train slowly and may take a very long time to convergeif the learning rate is too low. However, if the learning rate is too high, themodel might not converge at all or might converge to a less than idealoutcome. In addition, the model may oscillate and never converge if thelearning rate is improperly chosen. How to Pick a Good Learning Rate A key stage in training a machine learning model is selecting the appropriatelearning rate, and there are various ways to do this. Generally speaking, it isideal to begin with a low learning rate and then progressively increase it untilthe model begins to converge. Plotting the cost function as a function of iterations is a typical technique. Ifthe cost function starts to drop quickly but then oscillates, the learning ratecan be too high. In this situation, lowering the learning rate is likely to enhancethe model's performance.
Another method is to experiment with a range of learning rate values, such as0.001, 0.01, and 0.1. Plot the cost function as a function of the number ofiterations after running gradient descent for a few iterations for each value.The ideal learning rate for your model is probably the one that causes the costfunction to decrease with the greatest consistency and speed.
What Learning Rate Should Your Machine Learning Model Use?
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