Fix typos (tracel-ai#949)

hojjatabdollahi · Nov 12, 2023 · e255472 · e255472
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diff --git a/burn-book/src/basic-workflow/training.md b/burn-book/src/basic-workflow/training.md
@@ -59,7 +59,7 @@ impl<B: Backend> ValidStep<MNISTBatch<B>, ClassificationOutput<B>> for Model<B>
 Here we define the input and output types as generic arguments in the `TrainStep` and `ValidStep`.
 We will call them `MNISTBatch` and `ClassificationOutput`. In the training step, the computation of
 gradients is straightforward, necessitating a simple invocation of `backward()` on the loss. Note
-that contrary to PyTorch, gradients are not store alongside each tensor parameter, but are rather
+that contrary to PyTorch, gradients are not stored alongside each tensor parameter, but are rather
 returned by the backward pass, as such: `let gradients = loss.backward();`. The gradient of a
 parameter can be obtained with the grad function: `let grad = tensor.grad(&gradients);`. Although it
 is not necessary when using the learner struct and the optimizers, it can prove to be quite useful

diff --git a/burn-book/src/building-blocks/autodiff.md b/burn-book/src/building-blocks/autodiff.md
@@ -65,7 +65,7 @@ torch.no_grad():
 ```
 
 With Burn, you don't need to wrap the backend with the `Autodiff` for inference, and you
-can call `inner()` to obtain the inner tensor, which is useful for validation.ß
+can call `inner()` to obtain the inner tensor, which is useful for validation.
 
 ```rust, ignore
 /// Use `B: AutodiffBackend`