fix typo (#56)

README.md

@@ -15,11 +15,11 @@ We believe that the decentralized modular system is an interesting direction.
 
 The hope is that effective parts can be assembled together just like you assemble your own desktops.
 So the customized deep learning solution can be minimax, minimum in terms of dependencies,
-while maxiziming the users' need.
+while maximizing the users' need.
 
 NNVM offers one such part, it provides a generic way to do
 computation graph optimization such as memory reduction, device allocation and more
-while being agnostic to the operator interface defintion and how operators are executed.
+while being agnostic to the operator interface definition and how operators are executed.
 NNVM is inspired by LLVM, aiming to be a high level intermediate representation library
 for neural nets and computation graphs generation and optimizations.
 
@@ -32,7 +32,7 @@ This is essentially ***Unix philosophy*** applied to machine learning system.
 - Essential parts can be assembled in minimum way for embedding systems.
 - Developers can hack the parts they need and compose with other well defined parts.
 - Decentralized modules enable new extensions creators to own their project
-  without creating a monothilic version.
+  without creating a monolithic version.
 
 Deep learning system itself is not necessary one part, for example
 here are some relative independent parts that can be isolated

docs/overview.md

@@ -13,7 +13,7 @@ with the modular tools like CuDNN and CUDA, it is not hard to assemble a C++ API
 However, most users like to use python/R/scala or other languages.
 By registering the operators to NNVM, X can now get the graph composition
 language front-end on these languages quickly without coding it up for
-each type of langugage.
+each type of language.
 
 Y want to build a deep learning serving system on embedded devices.
 To do that, we need to cut things off, as opposed to add new parts,
@@ -97,7 +97,7 @@ Eventually the operator interface become big and have to evolve in the centraliz
 
 In NNVM, we decided to change the design and support arbitrary type of operator attributes,
 without need to change the operator registry. This also echos the need of minimum interface
-so that the code can be easier to share accross multiple projects
+so that the code can be easier to share across multiple projects
 
 User can register new attribute, such as inplace property checking function as follows.
 ```c++
@@ -122,7 +122,7 @@ NNVM_REGISTER_OP(exp)
 ```
 
 These attributes can be queried at arbitrary parts of the code, like the following parts.
-Under the hood, each attributes are stored in a any type columar store,
+Under the hood, each attributes are stored in a any type columnar store,
 that can easily be retrieved and cast back to typed table and do quick lookups.
 
 ```c++