Momentum

As explained in Natural Language Processing in Action, Second Edition

Momentum is an optimization hyperparameter that incorporates prior update direction to accelerate or damp parameter movement during gradient-based optimization.

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Defines acceleration and damping behavior, then demonstrates configuration; stops short of deriving the update equation or systematic tuning procedure.

Examples

Demonstrates momentum in a PyTorch single-neuron classifier trained for 200 epochs on vectorized name data.

What it assumes

Assumes gradient-based optimization, stochastic gradient descent, loss functions, and learning rate; introduces momentum as an optimizer attribute.

What you can do after

Apply momentum via PyTorch's SGD optimizer and inspect training loss and test accuracy across epochs.

How it connects

Connects momentum with stochastic gradient descent, learning rate, loss functions, backpropagation, and hyperparameter tuning.

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Also explained in

• 

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  Inside Deep Learning: Math, Algorithms, ModelsA stochastic gradient-based optimization method that uses an accumulated velocity term for parameter updates.
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  Sutskever's ListMomentum is an optimization technique that incorporates a running history of parameter updates to smooth and accelerate training.
• 

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  Math and Architectures of Deep LearningAn optimization strategy that accelerates parameter updates using accumulated past gradients or a lookahead variant to smooth descent on the loss surface.
• 

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  Deep Learning with R, Third EditionMomentum is an optimization technique that incorporates previous parameter updates into the current update to improve movement through the optimization landscape.
• 

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  Machine Learning Algorithms in DepthMomentum is an optimization technique that incorporates an exponentially weighted moving average of past gradients into parameter updates.

Compare how 6 books explain momentum →

Related concepts

• adamused by · Adam is an adaptive stochastic-gradient optimization algorithm that updates model parameters using moving estimates of the first and second moments of gradients.
• inner productuses · An inner product is a scalar-valued operation on two vectors that combines their corresponding components, summing their products and, for complex vectors, using conjugation.
• 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.
• partial derivativeequivalent to · A partial derivative is the derivative of a multivariable function with respect to one variable while holding the other variables constant.
• batch sizeuses · Batch size is the number of items grouped together for processing or computation as a single batch.
• Nesterov momentumincludes · Nesterov momentum is an optimization method that evaluates the gradient at a momentum-based lookahead position before updating model parameters.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.