Learning rate Calculator

Formula first

Overview

The learning rate is a scalar hyperparameter that determines the step size at each iteration of an optimization algorithm. It scales the gradient of the loss function, controlling how significantly the model's weights are adjusted in response to estimated error.

Symbols

Variables

$\alpha$ = Learning Rate

Apply it well

When To Use

When to use: Apply this during the training of machine learning models when using gradient-based optimization like SGD, Adam, or RMSProp. It is used to balance the trade-off between the speed of training and the precision of the convergence toward a minimum.

Why it matters: The learning rate is arguably the most critical hyperparameter; setting it too high causes the model to overshoot the minimum and diverge, while setting it too low results in inefficient training or getting stuck in local minima.

Avoid these traps

Common Mistakes

• Choosing a rate that is too large.
• Assuming one rate fits all models.

One free problem

Practice Problem

A machine learning practitioner is training a neural network with an initial learning rate alpha of 0.01. After observing that the loss is oscillating, they decide to reduce the learning rate to one-fifth of its current value. Calculate the new value for alpha.

Solve for: alpha

Hint: Divide the initial value by 5 to find the reduced rate.

The full worked solution stays in the interactive walkthrough.

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Related Formulas

References

Sources

1. Deep Learning (Goodfellow, Bengio, Courville)
2. Wikipedia: Learning rate
3. Wikipedia: Gradient descent
4. Pattern Recognition and Machine Learning (Bishop)
5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Chapter 8: Optimization for Training Deep Models.
6. A-Level Data & Computing — Machine Learning