Consider a [simple linear regression](../models/basics.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.
We want to update each parameter, using the gradient, in order to improve (reduce) the loss. Here's one way to do that:
```julia
using Flux.Tracker: data, grad
function update()
η = 0.1 # Learning Rate
for p in (W, b)
x, Δ = data(p), grad(p)
x .-= η .* Δ # Apply the update
Δ .= 0 # Clear the gradient
end
end
```
If we call `update`, the parameters `W` and `b` will change and our loss should go down.
There are two pieces here: one is that we need a list of trainable parameters for the model (`[W, b]` in this case), and the other is the update step. In this case the update is simply gradient descent (`x .-= η .* Δ`), but we might choose to do something more advanced, like adding momentum.
In this case, getting the variables is trivial, but you can imagine it'd be more of a pain with some complex stack of layers.
```julia
m = Chain(
Dense(10, 5, σ),
Dense(5, 2), softmax)
```
Instead of having to write `[m[1].W, m[1].b, ...]`, Flux provides a params function `params(m)` that returns a list of all parameters in the model for you.
For the update step, there's nothing whatsoever wrong with writing the loop above – it'll work just fine – but Flux provides various *optimisers* that make it more convenient.
An optimiser takes a parameter list and returns a function that does the same thing as `update` above. We can pass either `opt` or `update` to our [training loop](training.md), which will then run the optimiser after every mini-batch of data.
