SGD with Momentum
SGD with Momentum is an extension of Stochastic Gradient Descent that accelerates weight updates in the relevant direction by adding a fraction of the previous update vector to the current step.
Frequently Asked Questions
What analogy is commonly used to describe momentum in SGD?▼
A heavy ball rolling down a hill, which gathers speed and momentum as it descends, passing over minor bumps or local flat regions.
What is the role of the momentum coefficient parameter?▼
It determines how much of the past gradient history is kept. Usually set close to 0.9, it acts as a friction parameter that dampens oscillations.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationAccelerating neural network convergence, smoothing noisy gradient updates, and training computer vision models.
Coverage Trend12 Weeks
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