diff --git a/docs/src/gpu.md b/docs/src/gpu.md
index ee955f1b99..fd996afabb 100644
--- a/docs/src/gpu.md
+++ b/docs/src/gpu.md
@@ -126,7 +126,7 @@ julia> x |> cpu
 
 In order to train the model using the GPU both model and the training data have to be transferred to GPU memory. Moving the data can be done in two different ways:
 
-1. Iterating over the batches in a [DataLoader](@ref) object transferring each one of the training batches at a time to the GPU. This is recommended for large datasets. Done hand, it might look like this:
+1. Iterating over the batches in a [`DataLoader`](@ref) object transferring each one of the training batches at a time to the GPU. This is recommended for large datasets. Done hand, it might look like this:
    ```julia
    train_loader = Flux.DataLoader((X, Y), batchsize=64, shuffle=true)
    # ... model definition, optimiser setup
@@ -153,7 +153,7 @@ In order to train the model using the GPU both model and the training data have
    This is equivalent to `DataLoader(MLUtils.mapobs(gpu, (X, Y)); keywords...)`.
    Something similar can also be done with [`CUDA.CuIterator`](https://cuda.juliagpu.org/stable/usage/memory/#Batching-iterator), `gpu_train_loader = CUDA.CuIterator(train_loader)`. However, this only works with a limited number of data types: `first(train_loader)` should be a tuple (or `NamedTuple`) of arrays.
 
-2. Transferring all training data to the GPU at once before creating the [DataLoader](@ref) object. This is usually performed for smaller datasets which are sure to fit in the available GPU memory.
+2. Transferring all training data to the GPU at once before creating the `DataLoader`. This is usually performed for smaller datasets which are sure to fit in the available GPU memory.
    ```julia
    gpu_train_loader = Flux.DataLoader((X, Y) |> gpu, batchsize = 32)
    # ...