simplifies the title

doc/developer-guide/index.md

@@ -25,7 +25,7 @@ How to Read the Code
 - All the module interface are listed in [include](../../include), these interfaces are heavily documented.
 - You read the [Doxygen Version](https://mxnet.readthedocs.org/en/latest/doxygen) of the document.
 - Each module will only depend on other module by the header files in [include](../../include).
-- The implementation of module is in [src](../../src) folder. 
+- The implementation of module is in [src](../../src) folder.
 - Each source code only sees the file within its folder, [src/common](../../src/common) and [include](../../include).
 
 Most modules are mostly self-contained, with interface dependency on engine.
@@ -54,7 +54,7 @@ Open Source Design Notes
 ------------------------
 * [Programming Models for Deep Learning](../program_model.md)
 	- Compares various programming models, which motivates the current design.
-* [Dependency Engine for Multi-Device Deep Learning](note_engine.md)
+* [Dependency Engine for Deep Learning](note_engine.md)
 	- Introduces the dependency tracking and scheduling component for general deep learning,
 	  this motivates the design of Engine module.
 

doc/developer-guide/note_engine.md

@@ -1,5 +1,5 @@
-Make Multi-Device Deep Learning Easy with Dependency Engine
-===========================================================
+Dependency Engine for Deep Learning
+===================================
 One always important theme of deep learning libraries is to run faster and scale to larger
 dataset. In order to do so, a natural direction is always go beyond using one device(GPU),
 and make uses of more computation resources.

doc/index.md

@@ -25,15 +25,15 @@ Developer Guide
 
 Open Source Design Notes
 ------------------------
-This section contains the design document and notes we made for mxnet system design and deep learning 
+This section contains the design document and notes we made for mxnet system design and deep learning
 libraries in general. We believe that open sourcing the system design note, its motivations and choices
 can benefit general audience, for those who uses deep learning and who builds deep learning systems.
 
 This section will be updated with self-contained design notes on various aspect of deep learning systems,
 in terms of abstraction, optimization and trade-offs.
 
 * [Programming Models for Deep Learning](program_model.md)
-* [Dependency Engine for Multi-Device Deep Learning](developer-guide/note_engine.md)
+* [Dependency Engine for Deep Learning](developer-guide/note_engine.md)
 
 Indices and tables
 ------------------