Update README.md

README.md

@@ -53,6 +53,7 @@ The notMNIST dataset is a collection of images of letters from A to J in various
 
 ## Dataset Information
 
+  ```lua
   Number of Classes: 10 (A to J)
   Number of Samples: 187,24
   Image Size: 28 x 28 pixels
@@ -62,7 +63,8 @@ The notMNIST dataset is a collection of images of letters from A to J in various
 
 The dataset is split into a training set and a test set. Each class has its own subdirectory containing images of that class. The directory structure is as follows:
 
-  ```notMNIST/
+  ```lua
+  notMNIST/
   |-- train/
   |   |-- A/
   |   |-- B/
@@ -73,7 +75,7 @@ The dataset is split into a training set and a test set. Each class has its own
   |   |-- A/
   |   |-- B/
   |   |-- ...
-  |   |-- J/```
+  |   |-- J/
 
 
 ## Acknowledgements
@@ -86,11 +88,11 @@ The dataset is split into a training set and a test set. Each class has its own
 
 This is a pretty good dataset to train classifiers! According to Yaroslav:
 
-  > "Judging by the examples, one would expect this to be a harder task
+  > Judging by the examples, one would expect this to be a harder task
   than MNIST. This seems to be the case -- logistic regression on top of
   stacked auto-encoder with fine-tuning gets about 89% accuracy whereas
   same approach gives got 98% on MNIST. Dataset consists of small
   hand-cleaned part, about 19k instances, and large uncleaned dataset,
   500k instances. Two parts have approximately 0.5% and 6.5% label error
   rate. I got this by looking through glyphs and counting how often my
-  guess of the letter didn't match it's unicode value in the font file."
+  guess of the letter didn't match it's unicode value in the font file.