diff --git a/burn-book/src/basic-workflow/data.md b/burn-book/src/basic-workflow/data.md
index 962d6f8b3..4e3683c21 100644
--- a/burn-book/src/basic-workflow/data.md
+++ b/burn-book/src/basic-workflow/data.md
@@ -68,8 +68,8 @@ impl<B: Backend> Batcher<MnistItem, MnistBatch<B>> for MnistBatcher<B> {
     fn batch(&self, items: Vec<MnistItem>) -> MnistBatch<B> {
         let images = items
             .iter()
-            .map(|item| TensorData::from(item.image))
-            .map(|data| Tensor::<B, 2>::from_data(data.convert(), &self.device))
+            .map(|item| TensorData::from(item.image).convert::<B::FloatElem>())
+            .map(|data| Tensor::<B, 2>::from_data(data, &self.device))
             .map(|tensor| tensor.reshape([1, 28, 28]))
             // Normalize: make between [0,1] and make the mean=0 and std=1
             // values mean=0.1307,std=0.3081 are from the PyTorch MNIST example
@@ -119,8 +119,8 @@ images.
 ```rust, ignore
 let images = items                                                       // take items Vec<MnistItem>
     .iter()                                                              // create an iterator over it
-    .map(|item| TensorData::from(item.image))                            // for each item, convert the image to float32 data struct
-    .map(|data| Tensor::<B, 2>::from_data(data.convert(), &self.device)) // for each data struct, create a tensor on the device
+    .map(|item| TensorData::from(item.image).convert::<B::FloatElem>())  // for each item, convert the image to float data struct
+    .map(|data| Tensor::<B, 2>::from_data(data, &self.device))           // for each data struct, create a tensor on the device
     .map(|tensor| tensor.reshape([1, 28, 28]))                           // for each tensor, reshape to the image dimensions [C, H, W]
     .map(|tensor| ((tensor / 255) - 0.1307) / 0.3081)                    // for each image tensor, apply normalization
     .collect();                                                          // consume the resulting iterator & collect the values into a new vector
@@ -138,5 +138,6 @@ a targets tensor that contains the indexes of the correct digit class. The first
 the image array into a `TensorData` struct. Burn provides the `TensorData` struct to encapsulate
 tensor storage information without being specific for a backend. When creating a tensor from data,
 we often need to convert the data precision to the current backend in use. This can be done with the
-`.convert()` method. While importing the `burn::tensor::ElementConversion` trait, you can call
-`.elem()` on a specific number to convert it to the current backend element type in use.
+`.convert()` method (in this example, the data is converted backend's float element type
+`B::FloatElem`). While importing the `burn::tensor::ElementConversion` trait, you can call `.elem()`
+on a specific number to convert it to the current backend element type in use.
diff --git a/examples/guide/src/data.rs b/examples/guide/src/data.rs
index 78794d437..dd73209c4 100644
--- a/examples/guide/src/data.rs
+++ b/examples/guide/src/data.rs
@@ -24,8 +24,8 @@ impl<B: Backend> Batcher<MnistItem, MnistBatch<B>> for MnistBatcher<B> {
     fn batch(&self, items: Vec<MnistItem>) -> MnistBatch<B> {
         let images = items
             .iter()
-            .map(|item| TensorData::from(item.image))
-            .map(|data| Tensor::<B, 2>::from_data(data.convert::<B::FloatElem>(), &self.device))
+            .map(|item| TensorData::from(item.image).convert::<B::FloatElem>())
+            .map(|data| Tensor::<B, 2>::from_data(data, &self.device))
             .map(|tensor| tensor.reshape([1, 28, 28]))
             // normalize: make between [0,1] and make the mean =  0 and std = 1
             // values mean=0.1307,std=0.3081 were copied from Pytorch Mist Example