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microsoft · Mar 21, 2023 · 90c5b76 · 90c5b76
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diff --git a/website/docs/features/simple_deep_learning/about.md b/website/docs/features/simple_deep_learning/about.md
@@ -38,7 +38,7 @@ make it an excellent choice for SynapseML's Simple Deep Learning:
 - Flexibility: PyTorch Lightning retains the flexibility and expressiveness of PyTorch while adding useful abstractions to simplify the training loop and other boilerplate code.
 - Built-in Best Practices: PyTorch Lightning incorporates many best practices for deep learning, such as automatic optimization, gradient clipping, and learning rate scheduling, making it easier for users to achieve optimal performance.
 - Compatibility: PyTorch Lightning is compatible with a wide range of popular tools and frameworks, including Horovod, which allows users to easily use distributed training capabilities.
-- Rapid Development: With PyTorch Lightning, users can quickly prototype and experiment with different model architectures, even training strategies without worrying about low-level implementation details.
+- Rapid Development: With PyTorch Lightning, users can quickly experiment with different model architectures and training strategies without worrying about low-level implementation details.
 
 ### Sample usage with DeepVisionClassifier
 DeepVisionClassifier incorporates all models supported by [torchvision](https://github.com/pytorch/vision). 

diff --git a/website/versioned_docs/version-0.11.0/features/simple_deep_learning/about.md b/website/versioned_docs/version-0.11.0/features/simple_deep_learning/about.md
@@ -38,7 +38,7 @@ make it an excellent choice for SynapseML's Simple Deep Learning:
 - Flexibility: PyTorch Lightning retains the flexibility and expressiveness of PyTorch while adding useful abstractions to simplify the training loop and other boilerplate code.
 - Built-in Best Practices: PyTorch Lightning incorporates many best practices for deep learning, such as automatic optimization, gradient clipping, and learning rate scheduling, making it easier for users to achieve optimal performance.
 - Compatibility: PyTorch Lightning is compatible with a wide range of popular tools and frameworks, including Horovod, which allows users to easily use distributed training capabilities.
-- Rapid Development: With PyTorch Lightning, users can quickly prototype and experiment with different model architectures, even training strategies without worrying about low-level implementation details.
+- Rapid Development: With PyTorch Lightning, users can quickly experiment with different model architectures and training strategies without worrying about low-level implementation details.
 
 ### Sample usage with DeepVisionClassifier
 DeepVisionClassifier incorporates all models supported by [torchvision](https://github.com/pytorch/vision).