Update README.md

README.md

@@ -11,7 +11,7 @@ The project is divided into three main parts:
 - KNN, in this part we develop the knn program and test it to compare the evolution of the accuracy depending on the number of neighbors k. (the corresponding result is named accuracy_knn and is imported in the file 'results').
 - Mlp, this final part concerns the neural networks program and it also contains the comparison of the accuracy depending on the number of epoches.
 
-## data
+## Data
 
 The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
 
@@ -19,11 +19,11 @@ The dataset is divided into five training batches and one test batch, each with
 
 The classes are : [airplane,automobile,bird,cat,deer,dog,frog,horse,ship,truck]     
 
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+## KNN
 
+To develop this program, we used the L2 Euclidean distance.
+$$ d_2(I_{1} , I_{2}) = \sqrt{\sum{i=1}^{p}(I_{1}^p - I_{2}^p)^2} $$
+## To clone
 ```
 cd existing_repo
 git remote add origin https://gitlab.ec-lyon.fr/saidia/image-classification.git