Fix grammar in README, backend docs and initializers docs (keras-team…

…#12133)

* Update README.md

* Update backend.md

* Update initializers.py

* Update backend.md

README.md

@@ -27,7 +27,7 @@ Keras is compatible with: __Python 2.7-3.6__.
 
 - __User friendliness.__ Keras is an API designed for human beings, not machines. It puts user experience front and center. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
 
-- __Modularity.__ A model is understood as a sequence or a graph of standalone, fully configurable modules that can be plugged together with as few restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
+- __Modularity.__ A model is understood as a sequence or a graph of standalone, fully configurable modules that can be plugged together with as few restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are all standalone modules that you can combine to create new models.
 
 - __Easy extensibility.__ New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
 

docs/templates/backend.md

@@ -2,7 +2,7 @@
 
 ## What is a "backend"?
 
-Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle itself low-level operations such as tensor products, convolutions and so on. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. Rather than picking one single tensor library and making the implementation of Keras tied to that library, Keras handles the problem in a modular way, and several different backend engines can be plugged seamlessly into Keras.
+Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle low-level operations such as tensor products, convolutions and so on itself. Instead, it relies on a specialized, well optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. Rather than picking one single tensor library and making the implementation of Keras tied to that library, Keras handles the problem in a modular way, and several different backend engines can be plugged seamlessly into Keras.
 
 At this time, Keras has three backend implementations available: the **TensorFlow** backend, the **Theano** backend, and the **CNTK** backend.
 

keras/initializers.py

@@ -124,7 +124,7 @@ class TruncatedNormal(Initializer):
 
     These values are similar to values from a `RandomNormal`
     except that values more than two standard deviations from the mean
-    are discarded and re-drawn. This is the recommended initializer for
+    are discarded and redrawn. This is the recommended initializer for
     neural network weights and filters.
 
     # Arguments