update freeze docs

docs/src/models/advanced.md

@@ -39,23 +39,35 @@ However, doing this requires the `struct` to have a corresponding constructor th
 
 When it is desired to not include all the model parameters (for e.g. transfer learning), we can simply not pass in those layers into our call to `params`.
 
-Consider the simple multi-layer model where we want to omit optimising the first two `Dense` layers. This setup would look something like so:
+Consider a simple multi-layer perceptron model where we want to avoid optimising the first two `Dense` layers. We can obtain
+this using the slicing features `Chain` provides:
 
 ```julia
 m = Chain(
-  Dense(784, 64, σ),
-  Dense(64, 32),
-  Dense(32, 10), softmax)
+      Dense(784, 64, relu),
+      Dense(64, 64, relu),
+      Dense(32, 10)
+    )
 
 ps = Flux.params(m[3:end])
 ```
 
-`ps` now holds a reference to only the parameters of the layers passed to it.
+The `Zygote.Params` object `ps` now holds a reference to only the parameters of the layers passed to it.
 
-During training, now the gradients would only be applied to the last `Dense` layer (and the `softmax` layer, but that is stateless so doesn't have any parameters), so only that would have its parameters changed.
+During training, the gradients will only be computed for (and applied to) the last `Dense` layer, therefore only that would have its parameters changed.
 
 `Flux.params` also takes multiple inputs to make it easy to collect parameters from heterogenous models with a single call. A simple demonstration would be if we wanted to omit optimising the second `Dense` layer in the previous example. It would look something like this:
 
 ```julia
 Flux.params(m[1], m[3:end])
 ```
+
+Sometimes, a more fine-tuned control is needed. 
+We can freeze a specific parameter of a specific layer which already entered a `Params` object `ps`, 
+by simply deleting it from `ps`:
+
+```julia
+ps = params(m)
+delete!(ps, m[2].b) 
+```
+