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About Lecture Note SGD
This document provides an overview of stochastic gradient descent (SGD) and simulated annealing for optimization problems. SGD iteratively estimates the gradient using a single random sample to update the parameters. It converges to local optima under certain step size conditions. Noisy SGD adds noise and can converge to global optima as noise increases. SGD is illustrated using mean estimation. Simulated annealing uses a temperature-based probability distribution over solutions during iterations to approximate global optima.
Detailed Information
| Author: | ['Nishant Panda'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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