What is backpropagation in AI training?

Backprop uses the chain rule to propagate error backward through layers, computing gradients that reveal each weight's error contribution.

How does the AI training loop work step by step?

Forward pass guesses; loss measures wrongness; backprop finds culprits; weights update via gradient descent. Loop millions of times.

Why is learning rate so critical in neural networks?

It controls step size down the loss hill — too big overshoots, too small stalls training indefinitely.

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The AI Training Loop: Millions of Tiny Failures That Birth Intelligence

Forget rulebooks. AI masters poetry, translation, and gadget-spotting by bombing spectacularly, then tweaking. Here's the four-step loop making it happen.

theAIcatchup Apr 07, 2026 4 min read

Diagram of AI training loop showing forward pass, loss, backpropagation, and weight update steps in a cycle

⚡ Key Takeaways

AI learns via a four-step training loop mimicking trial-and-error, not hardcoded rules. 𝕏
Backpropagation's chain rule efficiently assigns blame to specific weights. 𝕏
Gradient descent navigates loss landscapes, with learning rate as the key tuner. 𝕏

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#AI training loop #backpropagation #gradient descent #neural network training

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Originally reported by dev.to

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