diff --git a/_posts/2018-05-02-road-to-1.0.md b/_posts/2018-05-02-road-to-1.0.md
index 4b72cc3c66df..30b1fe05788b 100644
--- a/_posts/2018-05-02-road-to-1.0.md
+++ b/_posts/2018-05-02-road-to-1.0.md
@@ -11,9 +11,9 @@ We would like to give you a preview of the roadmap for PyTorch 1.0 , the next re
 
 However, 1.0 isn't just about stability of the interface.
 
-One of PyTorch's biggest strengths is it's first-class Python integration, it's imperative style, it's simplicity in API and options. These are aspects that make PyTorch good for research and hackability.
+One of PyTorch's biggest strengths is its first-class Python integration, imperative style, simplicity of the API and options. These are aspects that make PyTorch good for research and hackability.
 
-One of it's biggest downsides has been production-support. What we mean by production-support is the countless things one has to do to models to run them efficiently at massive scale:
+One of its biggest downsides has been production-support. What we mean by production-support is the countless things one has to do to models to run them efficiently at massive scale:
 
 - exporting to C++-only runtimes for use in larger projects
 - optimizing mobile systems on iPhone, Android, Qualcomm and other systems
@@ -35,7 +35,7 @@ To pull this off, we are introducing `torch.jit`,  a just-in-time (JIT) compiler
 
 > **In 1.0, your code continues to work as-is, we're not making any big changes to the existing API.**
 
-Making your model production-ready is an opt-in annotation, which uses the `torch.jit` compiler to export your model to a Python-less environment, and improving it's performance. Let's walk through the JIT compiler in detail.
+Making your model production-ready is an opt-in annotation, which uses the `torch.jit` compiler to export your model to a Python-less environment, and improving its performance. Let's walk through the JIT compiler in detail.
 
 ## `torch.jit`: A JIT-compiler for your models