Stochastic Gradient Descent (SGD) is an optimization technique used to reduce a model’s loss and improve its performance. A good way to imagine it is like walking down a hill to reach the lowest point. The gradient tells you the slope and the direction to move downward. But instead of looking at the entire landscape (the full dataset), SGD looks at small random batches of data to estimate the slope at each step. Because it works with smaller samples, the updates are quicker and more frequent, al

[PDF] Adam: A Method for Stochastic Optimization | Semantic Scholar

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Gradient descent, how neural networks learn | Deep learning, chapter 2 on Make a GIF

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