Update INTRO.md

Author: tango4j

INTRO.md

@@ -2,8 +2,6 @@
 
 This repository aims for comparative analysis of TensorFlow vs PyTorch, for those who want to learn TensorFlow while already familiar with PyTorch or vice versa.
 
-The whole content was written in Ipython Notebook then converted into MarkDown. Ipython Notebooks in main directory cotains the same content.
-
 ## TABLE OF CONTENTS
 
 [**01. Tensor**](https://github.com/tango4j/tensorflow-vs-pytorch#01-tensor)   
@@ -130,53 +128,3 @@ The whole content was written in Ipython Notebook then converted into MarkDown.
 
 - There are a few distinct differences between Tensorflow and Pytorch when it comes to data compuation.
 
-|               | TensorFlow      | PyTorch        |
-|---------------|-----------------|----------------|
-| Framework     | Define-and-run  | Define-by-run  |
-| Graph         | Static | Dynamic|
-| Debug         | Non-native debugger (tfdbg) |pdb(ipdb) Python debugger|
-
-**How "Graph" is defined in each framework?**
-
-#**TensorFlow:** 
-
-- Static graph.
-
-- Once define a computational graph and excute the same graph repeatedly.
-
-- Pros: 
-
-    (1) Optimizes the graph upfront and makes better distributed computation.
-    
-    (2) Repeated computation does not cause additional computational cost.
-
-
-- Cons: 
-
-    (1) Difficult to perform different computation for each data point.
-    
-    (2) The structure becomes more complicated and harder to debug than dynamic graph. 
-
-
-#**PyTorch:** 
-
-- Dynamic graph.
-
-- Does not define a graph in advance. Every forward pass makes a new computational graph.
-
-- Pros: 
-
-    (1) Debugging is easier than static graph.
-    
-    (2) Keep the whole structure concise and intuitive. 
-    
-    (3) For each data point and time different computation can be performed.
-    
-    
-- Cons: 
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-    (1) Repetitive computation can lead to slower computation speed. 
-    
-    (2) Difficult to distribute the work load in the beginning of training.
-
-