I Built a Neural Network Training Loop in 5 Lines Using JAX - Day 2
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SERIES: Learning RL and JAX in Public - from zero to DeepMind :) On Day 1 I covered what JAX is and the three superpowers: GPU acceleration, grad, and jit. Today I actually used them. And the moment where I manually verified a gradient by hand and watched JAX confirm it is the one that made everything real for me. But before the code, I had to understand what a gradient actually is. Because I realised I had been using them for years without a clean mental model. What is a derivative, really?…
1Key Takeaways
- SERIES: Learning RL and JAX in Public - from zero to DeepMind :) On Day 1 I covered what JAX is and the three superpowers: GPU acceleration, grad, and jit.
- And the moment where I manually verified a gradient by hand and watched JAX confirm it is the one that made everything real for me.
- But before the code, I had to understand what a gradient actually is.
- Because I realised I had been using them for years without a clean mental model.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that sERIES: Learning RL and JAX in Public - from zero to DeepMind :) On Day 1 I covered what JAX is and the three superpowers: GPU acceleration, grad, and jit.
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