Update README.md

README.md

@@ -53,17 +53,16 @@ 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
-  Color Channels: Grayscale
+- Number of Classes: 10 (A to J)
+- Number of Samples: 187,24
+- Image Size: 28 x 28 pixels
+- Color Channels: Grayscale
 
 ## Dataset Structure
 
 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:
 
-  ```lua
+  ```
   notMNIST/
   |-- train/
   |   |-- A/
@@ -76,23 +75,10 @@ The dataset is split into a training set and a test set. Each class has its own
   |   |-- B/
   |   |-- ...
   |   |-- J/
-
+  ```
 
 ## Acknowledgements
 
   http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html
   https://www.kaggle.com/datasets/lubaroli/notmnist
 
-
-## Inspiration
-
-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
-  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.