Fix typos

README.md

@@ -91,16 +91,19 @@ With `uTensorGraphMatcher`, performing common subgraph tasks such as isomorphic
 
 Note: we'll use operation/node/layer interchangeably in the documentation
 
-- It's commonly seen pattern in convolution neural network (`CNN`), `conv -> relu -> pooling`. That is, a 2D convolution followed by a relu layer and then a pooliing down sampling layer. With our `uTensorGraphMatcher`, you can locate such pattern in your `CNN` model and fuse/replace matched nodes into one optimized `QuantConvPool` node.
+- It's commonly seen pattern in convolution neural network (`CNN`), `conv -> relu -> pooling`. That is, a 2D convolution followed by a relu layer and then a pooling down sampling layer.
+- With our `uTensorGraphMatcher`, you can locate such pattern in your `CNN` model and fuse/replace matched nodes into one optimized `QuantizedFusedConv2DMaxpool` node.
     - Left: original graph
     - Middle: matched convolution layer
-    - Right: replace the matched layer with specialized `QuantConvPool` node
+    - Right: replace the matched layer with specialized `QuantizedFusedConv2DMaxpool` node
 
 ![conv-pool-fuce](images/conv_pool_fuse.png)
 
 ### Dropout Layer Removal
 
-- Though `dropout` is an effective technique to improve training performance of your model, it's not necessary during inference phrase. In the mainstream frameworks such as `Tensorflow` or `PyTorch`, an `dropout` layer is typically implemented with other elementary operations/nodes. As a result, finding and removing those nodes for interence optimization (both in model size and prediciton time) is not trivial and error prone. With our `uTensorGraphMatcher`, you can find and remove the dropout nodes as illustrated in the following picture.
+- Though `dropout` is an effective technique to improve training performance of your model, it's not necessary during inference phrase.
+- In the mainstream frameworks such as `Tensorflow` or `PyTorch`, an `dropout` layer is typically implemented with other elementary operations/nodes. As a result, finding and removing those nodes for interence optimization (both in model size and prediciton time) is not trivial and error prone.
+- With our `uTensorGraphMatcher`, you can find and remove the dropout nodes as illustrated in the following picture.
     - Left: original graph with dropout Layers
     - Middle: matched dropout layers
     - Right: graph with dropout layers removed