{"id":25074,"date":"2025-12-08T09:59:51","date_gmt":"2025-12-08T09:59:51","guid":{"rendered":"https:\/\/gtracademy.org\/?p=25074"},"modified":"2025-12-08T09:59:51","modified_gmt":"2025-12-08T09:59:51","slug":"gradient-descent-a-beginner-friendly-guide","status":"publish","type":"post","link":"https:\/\/gtracademy.org\/staging\/gradient-descent-a-beginner-friendly-guide\/","title":{"rendered":"Gradient Descent A Beginner-Friendly Guide to How Models Learn\u00a0Best for 2025"},"content":{"rendered":"

Most modern ML models<\/strong><\/a> from simple regressions to deep neural networks learn using the core idea i.e. Gradient Descent. It\u2019s an optimization method that adjusts model parameters gradually to minimize the error, very similar to like walking downhill till you reach the lowest point of a hill.<\/span>\u200b<\/span>\u00a0<\/span><\/p>\n

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\"Gradient<\/p>\n

Rolling ball down the hill \u2013 An analogy<\/span><\/b>\u00a0<\/span><\/h2>\n

Imagine this you are standing on a hazy mountain and want to reach the bottom of the valley. You are only able to see the ground right around your feet. You can feel which direction slopes downward and take a small step that way, then you keep repeating this process again. This is what Gradient Descent exactly does: at every step, it calculates at the \u201cslope\u201d of the loss function and then moves the parameters of the model in the direction that minimizes the loss.<\/span>\u200b<\/span>\u00a0<\/span><\/p>\n

In this example:<\/span>\u00a0<\/span><\/p>\n