corrected some minor typos

doc/program_model.md

@@ -371,7 +371,7 @@ What we usually observe is that it is usually helpful to write parameter updates
 while the gradient calculations can be done more effectively in symbolic programs.
 
 The mix of programs is actually happening in existing symbolic libraries, because python itself is imperative.
-For example, the following programs mixed the symbolic part together with numpy(which is imperative).
+For example, the following program mixes the symbolic part together with numpy(which is imperative).
 ```python
 A = Variable('A')
 B = Variable('B')

doc/python/model.md

@@ -1,6 +1,6 @@
 MXNet Python Model API
 ======================
-The model API in mxnet as not really an API.
+The model API in mxnet is not really an API.
 It is a thin wrapper build on top of [ndarray](ndarray.md) and [symbolic](symbol.md)
 modules to make neural network training easy.
 

example/README.md

@@ -25,7 +25,7 @@ IPython Notebooks
 -----------------
 * [Predict with Pre-trained model](https://github.com/dmlc/mxnet/blob/master/example/notebooks/predict-with-pretrained-model.ipynb) - Notebook on how to predict with pretrained model.
 * [composite symbol](notebooks/composite_symbol.ipynb) - A demo of how to composite a symbolic Inception-BatchNorm Network
-* [cifar-10 recipe](notebooks/cifar-recipe.ipynb) - A step by step demo of how to use MXNet
+* [cifar-10 recipe](notebooks/cifar10-recipe.ipynb) - A step by step demo of how to use MXNet
 * [cifar-100](notebooks/cifar-100.ipynb) - A demo of how to train a 75.68% accuracy CIFAR-100 model
 * [simple bind](notebooks/simple_bind.ipynb) - A demo of low level training API.