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docs/basics.md: Add tracked after 1.0

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Ayan Banerjee 2019-01-22 23:37:34 +05:30 committed by GitHub
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docs/src/models/basics.md
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@ -28,7 +28,7 @@ When a function has many parameters, we can pass them all in explicitly:
f(W, b, x) = W * x + b
Tracker.gradient(f, 2, 3, 4)
(4.0 (tracked), 1.0, 2.0 (tracked))
(4.0 (tracked), 1.0 (tracked), 2.0 (tracked))
```
But machine learning models can have *hundreds* of parameters! Flux offers a nice way to handle this. We can tell Flux to treat something as a parameter via `param`. Then we can collect these together and tell `gradient` to collect the gradients of all of them at once.