Learning rate

As explained in Designing Deep Learning Systems: A guide for software engineers

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

A learning rate controls the step size used to update model parameters during optimization. After backpropagation calculates gradients, an optimization algorithm such as stochastic gradient descent uses those gradients and the learning rate to update weights and biases. section 3.2.1

Treat the learning rate as an operational training setting:

• It influences how quickly training progresses and affects the model’s eventual prediction accuracy. section 5.1.2
• Evaluate it within the complete training loop: prediction, loss calculation, backpropagation, parameter update, and model saving. section 3.2.1
• Tune it alongside batch size, regularization, activation functions, layer count, and neuron count rather than treating it as an isolated choice. section 5.1.2

Experimenting with a spiral classifier

In TensorFlow Playground, vary the learning rate while configuring a six-layer network with five neurons per layer, ReLU activation, batch size 10, and L1 regularization. Compare training time, learning curves, and classification performance after roughly 500 epochs. section 5.1.2

Figure 3.3 General steps of a deep learning model training workflow

Use the training-workflow figure to locate the learning rate at the parameter-update stage, after gradients are calculated. section 3.2.1

How Designing Deep Learning Systems: A guide for software engineers covers it

How deep it goes

Explains its workflow position and effects on training speed and accuracy, but does not derive the update rule or internal optimizer mechanics.

Examples

Demonstrates tuning through TensorFlow Playground and a spiral-classification configuration with specified layers, activation, batch size, and regularization.

What it assumes

Uses predictions, loss, gradients, backpropagation, and optimization algorithms as the surrounding training workflow.

What you can do after

Apply it via TensorFlow Playground experiments that compare learning behavior, training time, and classification performance.

How it connects

Connects hyperparameter optimization with backpropagation, gradients, stochastic gradient descent, model accuracy, and training curves.

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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 Designing Deep Learning Systems: A guide for software engineers. The section links point to the parts of the book it draws on.