Gradient descent

After this lesson you can: Describe how a model walks downhill to better parameters.

First: Loss: how wrong is the model?, What a derivative actually is

Watch

• Gradient descent, how neural networks learn | Deep Learning Chapter 2 - 3Blue1Brown, 21 min. Visual, beginner-friendly concept video linking gradients to how models improve parameters iteratively.
• Gradient Descent Explained - IBM Technology, 7 min. Short industry explainer likely reinforces the downhill-search mental model without heavy math.
• Gradient Descent, Step-by-Step - StatQuest with Josh Starmer, 24 min. Step-by-step framing fits engineers learning the update loop after loss and derivatives.

Notes

Gradient descent is the basic loop many models use to improve themselves. A model starts with some parameters, uses them to make predictions, measures how bad those predictions are with a loss function, then changes the parameters a little to reduce that loss. It does not usually compute the perfect parameters in one shot. It repeatedly tries, measures, and adjusts.

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