Learning Rate — Technology Wiki

Overview

Direct Answer

Learning rate is a hyperparameter that determines the magnitude of parameter updates during gradient descent optimisation. It directly scales the gradient signal, controlling how aggressively the model adjusts weights at each training iteration.

How It Works

During backpropagation, the model computes the loss gradient with respect to each parameter. The optimiser multiplies this gradient by the learning rate before applying the update. A rate of 0.01 means parameters shift by 1% of the gradient magnitude; 0.001 by 0.1%. This multiplicative factor fundamentally controls convergence speed and stability.

Why It Matters

Selecting an appropriate value directly impacts training efficiency, final model accuracy, and computational cost. Rates that are too high cause divergence or oscillation around optimal weights; rates too low extend training time unnecessarily, increasing infrastructure expenses and time-to-deployment.

Common Applications

Applied across neural network training in computer vision, natural language processing, and time series forecasting. Practitioners routinely adjust this parameter when training image classifiers, language models, and recommendation systems to balance convergence speed against solution quality.

Key Considerations

Optimal values vary significantly across datasets, model architectures, and optimiser algorithms (SGD versus Adam require different ranges). Many practitioners employ learning rate schedules or adaptive methods that adjust the rate dynamically during training rather than using static values.

Referenced By1 term mentions Learning Rate

Other entries in the wiki whose definition references Learning Rate — useful for understanding how this concept connects across Machine Learning and adjacent domains.

Adam Optimiser·Machine Learning

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Backpropagation

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

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