check docs

README.md

@@ -10,31 +10,31 @@
 [中文](README_zh.md)
 
 **Mobile AI Compute Engine** (or **MACE** for short) is a deep learning inference framework optimized for
-mobile heterogeneous computing platforms. The design is focused on the following
+mobile heterogeneous computing platforms. The design focuses on the following
 targets:
 * Performance
   * The runtime is highly optimized with NEON, OpenCL and Hexagon, and
     [Winograd algorithm](https://arxiv.org/abs/1509.09308) is introduced to
-    speed up the convolution operations. Except for the inference speed, the
-    initialization speed is also intensively optimized.
+    speed up the convolution operations. Besides the fast inference speed, the
+    initialization part is also intensively optimized to be faster.
 * Power consumption
   * Chip dependent power options like big.LITTLE scheduling, Adreno GPU hints are
-    included as advanced API.
+    included as advanced APIs.
 * Responsiveness
-  * UI responsiveness gurantee is sometimes obligatory when runing a model.
+  * UI responsiveness guarantee is sometimes obligatory when running a model.
     Mechanism like automatically breaking OpenCL kernel into small units is
     introduced to allow better preemption for the UI rendering task.
 * Memory usage and library footprint
-  * Graph level memory allocation optimization and buffer reuse is supported.
+  * Graph level memory allocation optimization and buffer reuse are supported.
     The core library tries to keep minium external dependencies to keep the
     library footprint small.
 * Model protection
-  * Model protection is one the highest priority feature from the beginning of 
+  * Model protection is the highest priority feature from the beginning of 
     the design. Various techniques are introduced like coverting models to C++
     code and literal obfuscations.
 * Platform coverage
   * A good coverage of recent Qualcomm, MediaTek, Pinecone and other ARM based
-    chips. CPU runtime is also compitable with most POSIX systems and
+    chips. CPU runtime is also compatible with most POSIX systems and
     archetectures with limited performance.
 
 ## Getting Started
@@ -44,7 +44,7 @@ targets:
 
 ## Performance
 [MACE Model Zoo](https://github.com/XiaoMi/mace-models) contains
-several common neural networks models and built daily against a list of mobile
+several common neural networks and models which will be built daily against a list of mobile
 phones. The benchmark result can be found in the CI result page.
 
 ## Communication

docs/development/contributing.md

@@ -4,7 +4,7 @@ Contributing guide
 License
 -------
 
-The source file should contains a license header. See the existing files
+The source file should contain a license header. See the existing files
 as the example.
 
 Python coding style

docs/getting_started/create_a_model_deployment.rst

@@ -6,15 +6,15 @@ file.
 
 One deployment file describes a case of model deployment,
 each file will generate one static library (if more than one ABIs specified,
-there will be one static library for each). The deployment file can contains
-one or more models, for example, a smart camera application may contains face
+there will be one static library for each). The deployment file can contain
+one or more models, for example, a smart camera application may contain face
 recognition, object recognition, and voice recognition models, which can be
 defined in one deployment file),
 
 
 Example
 ----------
-Here is an deployment file example used by Android demo application.
+Here is an example deployment file used by an Android demo application.
 
 TODO: change this example file to the demo deployment file
 (reuse the same file) and rename to a reasonable name.