Under the hood, gradient descent is a tight loop of four moves repeated thousands of times. 🔁

Under the hood, gradient descent is a tight loop of four moves repeated thousands of times. 🔁

Forward: predict and measure the loss. Backward: get the gradient. Step: nudge the parameters downhill. Repeat. Once you see the loop, the famous variants — batch, stochastic, mini-batch — are just choices about how much data you look at before each step.

This post is the engine room: the real mechanics, the math made concrete, and the three flavors you'll actually use.

INSIDE THIS POST:
• the four-step loop, precisely
• how the gradient is computed from the loss
• batch vs stochastic vs mini-batch
• why mini-batch wins in practice
• momentum and adaptive steps in one line
• convergence: how you know it's done

This is the part that makes the loop click. Save it. 👇

📌 Day 13 of 100 · Post 3 of 5 · Category: Math for ML

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🔗 LINKS
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Видео Under the hood, gradient descent is a tight loop of four moves repeated thousands of times. 🔁 канала Saurav Danej