How deeply does Grokking AI Algorithms, Second Edition cover learning rate?

Explains update-size effects and gradient-descent placement, then stops short of detailed optimization theory.

Learning rate

As explained in Grokking AI Algorithms, Second Edition

A learning rate is a training hyperparameter that controls the step size used to update model parameters.

A learning rate is a training hyperparameter that controls how large a step model weights take during backpropagation. Small steps can make training slow; large steps can make updates chaotic. section 9.18 section 11.35

The rate is part of gradient descent: after calculating the gradient, training moves in the opposite direction, with the learning rate determining the step size. section 9.26

Choosing a learning rate involves balancing speed, stability, and experimentation:

Use a slow rate for smaller, steadier updates, while recognizing that training may take longer. section 9.18
Treat a high rate cautiously because dramatic weight changes can produce chaotic training. section 9.18
Adjust the rate when training stagnates, using experimentation or a tuning strategy. section 9.18 section 11.35

Comparing candidate values

A tuning run might compare learning rates of 0.01, 0.001, and 0.0001. Grid search trains every selected combination, while random search tests a fixed number of randomly chosen combinations and can explore more efficiently. section 11.35

How Grokking AI Algorithms, Second Edition covers it

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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 Grokking AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.