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About Deep Learning
Stochastic gradient descent (SGD) estimates the gradient descent by computing the average loss over a small random sample of the training data rather than the entire data set. This makes SGD faster than full gradient descent but less accurate, requiring more iterations and smaller step sizes. Important considerations for SGD include ensuring the input data and initial weights have zero mean and equal variance, as well as hyperparameters like the initial learning rate, learning rate decay, momentum, batch size, and weight initialization. While SGD is very fast, it is also an imperfect optimizer and techniques like momentum and learning rate decay can improve its performance.
Detailed Information
| Author: | ['Daniel Lindberg'] |
|---|---|
| Publication Year: | 2017 |
| Pages: | 1 |
| Language: | English |
| Format: | |
| Price: | FREE |
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