Grammatical Doc Changes (#2567)

Author: hasn77

docs-guides/source/comparing-ml-programs-and-neural-networks.md

@@ -9,7 +9,7 @@
 
 As ML models evolve in sophistication and complexity, their [representations](#model-representations) are also evolving to describe how they work. _ML programs_ are models that are represented as operations in code. The ML program model type is the foundation for future Core ML improvements.
 
-A few of the major the differences between a neural network and an ML program are as follows:
+A few of the major differences between a neural network and an ML program are as follows:
 
 | Neural Network | ML Program |
 | ----------- | ----------- |
@@ -42,7 +42,7 @@ Converting to an ML program provides the following advantages over converting to
 
 - More efficient compiling:
 
-    Since ML programs do not store weights in the protobuf file format, the models are more memory-efficient to compile. You can  significantly improve performance by using Core ML's on-device compilation API. For details, see [Downloading and Compiling a Model on the User’s Device](https://developer.apple.com/documentation/coreml/core_ml_api/downloading_and_compiling_a_model_on_the_user_s_device).
+    Since ML programs do not store weights in the protobuf file format, the models are more memory-efficient to compile. You can significantly improve performance by using Core ML's on-device compilation API. For details, see [Downloading and Compiling a Model on the User’s Device](https://developer.apple.com/documentation/coreml/core_ml_api/downloading_and_compiling_a_model_on_the_user_s_device).
 
 ## Model Representations
 

docs-guides/source/convert-a-pytorch-segmentation-model.md

@@ -126,7 +126,7 @@ Now that the PyTorch model is segmenting the image correctly, you can trace the
 
 However, the model returns a dictionary. If you try to use `trace = torch.jit.trace(model, input_batch)` without first extracting the output you want from the dictionary, the tracer outputs an error: `Only tensors or tuples of tensors can be output from traced functions`.
 
-To sidestep this limitation, you can wrap the model in a module that extracts the output  from the dictionary:
+To sidestep this limitation, you can wrap the model in a module that extracts the output from the dictionary:
 
 ```python
 class WrappedDeeplabv3Resnet101(nn.Module):