Open Access
Table 2
Comparison between GD and SGD.
| Gradient Descent | Stochastic Gradient Descent |
|---|---|
 |
 |
| GD computes gradient using a batch from the dataset | SGD computes gradients using single rows of training examples |
| It follows a deterministic approach | It follows a random approach |
| It converges slower on large training samples | It converges faster on large training samples |
|
Steps:
For every iteration – Traverse entire dataset – Evaluate gradient – Return |
Steps:
For every iteration – Iterate over each value in the dataset – Evaluate Gradient – Return |
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