Minor change for README

README.md

@@ -1,6 +1,6 @@
 # TensorSwift
 
-_TensorSwift_ is a lightweight library to calculate tensors, which has similar APIs to [_TensorFlow_](https://www.tensorflow.org/)'s. _TensorSwift_ is useful to simulate calculating tensors in Swift using models trained by _TensorFlow_.
+_TensorSwift_ is a lightweight library to calculate tensors, which has similar APIs to [_TensorFlow_](https://www.tensorflow.org/)'s. _TensorSwift_ is useful to simulate calculating tensors in Swift __using models trained by _TensorFlow___.
 
 ```swift
 let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6])
@@ -19,7 +19,7 @@ let ones = Tensor(shape: [2, 3, 4], element: 1)
 
 ![deep-mnist.gif](Resources/DeepMnist.gif)
 
-The following is how to simulate [Deep MNIST for Experts](https://www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/index.html), a tutorial of _TensorFlow_, by _TensorSwift_.
+The following code shows how to simulate [Deep MNIST for Experts](https://www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/index.html), a tutorial of _TensorFlow_, by _TensorSwift_.
 
 ```swift
 public struct Classifier {
@@ -31,25 +31,25 @@ public struct Classifier {
     public let b_fc1: Tensor
     public let W_fc2: Tensor
     public let b_fc2: Tensor
-    
+
     public func classify(x_image: Tensor) -> Int {
         let h_conv1 = (x_image.conv2d(filter: W_conv1, strides: [1, 1, 1]) + b_conv1).relu
         let h_pool1 = h_conv1.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1])
-        
+
         let h_conv2 = (h_pool1.conv2d(filter: W_conv2, strides: [1, 1, 1]) + b_conv2).relu
         let h_pool2 = h_conv2.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1])
-        
+
         let h_pool2_flat = h_pool2.reshape([1, 7 * 7 * 64])
         let h_fc1 = (h_pool2_flat.matmul(W_fc1) + b_fc1).relu
-        
+
         let y_conv = (h_fc1.matmul(W_fc2) + b_fc2).softmax
 
         return y_conv.elements.enumerate().maxElement { $0.1 < $1.1 }!.0
     }
 }
 ```
 
-## Instration
+## Installation
 
 ### CocoaPods