Learning rate

As explained in Data Without Labels

A learning rate is a scalar hyperparameter that controls the size of parameter updates during iterative training.

A learning rate is a scalar that sets the size of corrective weight updates during training. Larger values can reduce accuracy, while smaller values increase training time. section 8.2.6

Use the learning rate to reason about training behavior:

• Higher learning rates take larger corrective steps but may produce lower accuracy. section 8.2.6
• Very low learning rates make training take longer. section 8.2.6
• Weight updates adjust parameters with respect to loss gradients. section 8.2.6

Boltzmann learning

In a Boltzmann network, the learning rate scales weight corrections based on the difference between visible–hidden co-activation in the data distribution and the model distribution. section 8.8.1

Figure 8.8 The concept of gradient descent. It is the mechanism to minimize the loss function.

Use the gradient-descent figure to visualize how update steps minimize the loss function. section 8.2.6

How Data Without Labels covers it

How deep it goes

Defines update size and training tradeoffs, then applies the parameter to a Boltzmann weight-update rule.

Examples

Illustrates corrective network steps and visible–hidden correlation differences in Boltzmann weight updates.

What you can do after

Evaluate how update size affects accuracy and training time in gradient-based neural-network training.

How it connects

Connects learning rate to gradient descent, loss gradients, momentum, NAG, and Boltzmann learning rules.

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Also explained in

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Related concepts

• hyperparameterkind of · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
• gradient descentconfigures · Gradient descent is an iterative optimization method that updates parameters opposite the gradient of an objective function to reduce its value.
• learning rate scheduleconfigured by · A learning rate schedule is a rule or function that determines how an optimizer’s learning rate changes during training.
• stochastic gradient descentconfigures · Stochastic gradient descent is an iterative optimization method that updates parameters using gradients estimated from individual examples or randomly selected small batches.
• cosine annealingconfigured by · Cosine annealing is a learning-rate scheduling method that varies the learning rate over training according to a cosine-shaped curve.

This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.