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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…
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Derivatives and Gradients — The Mechanism Behind “Learning” (Ep:02.02)
Every neural network “learns” by following a gradient downhill. Module 2 of From Zero to Agents builds derivatives, partial derivatives, and the chain rule from first principles, verifies them against…
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Recent Posts
- Why Learning Rate Matters: Gradient Descent Convergence, Proven With a Closed-Form Equation (Ep:02.03)
- Derivatives and Gradients — The Mechanism Behind “Learning” (Ep:02.02)
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