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Why Your Training Loss Exploded to NaN: The Math of Learning Rate, Not Guesswork
A loss curve that spikes to NaN mid-training isn’t random instability — it’s a provable consequence of crossing an exact convergence threshold. Here’s the derivation, and what it means for…
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Why Learning Rate Matters: Gradient Descent Convergence, Proven With a Closed-Form Equation (Ep:02.03)
A learning rate that’s too large doesn’t just train slower — it can make loss diverge to infinity, provably. Module 2 of From Zero to Agents derives the exact convergence…
