diff --git a/README.md b/README.md
index c556f332a45d95d29ca8f6ea601e3a7d90d64c8d..a6a6de7e5881aa54cf87d78277675170d9c7605d 100644
--- a/README.md
+++ b/README.md
@@ -7,43 +7,24 @@ MOD 4.6 Deep Learning & Artificial Intelligence
 The objective of this tutorial is to write a complete image classification program in Python.
 Two classification models will be successively developed and tested: k-nearest neighbors (KNN) and neural networks (NN).
 
-## Add your files
+## Requirements
 
-- [ ] [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:
+In this tutorial we use Python 3.7 or higher and the library numpy.
 
-```
-cd existing_repo
-git remote add origin https://gitlab.ec-lyon.fr/marab/image-classification.git
-git branch -M main
-git push -uf origin main
-```
+## Usage
 
-## Integrate with your tools
+The image database used for the experiments is CIFAR-10 which consists of 60 000 color images of size 32x32 divided into 10 classes (plane, car, bird, cat, ...).
+This database can be obtained at the address https://www.cs.toronto.edu/~kriz/cifar.html where are also given the indications to read the data.
 
-- [ ] [Set up project integrations](https://gitlab.ec-lyon.fr/marab/image-classification/-/settings/integrations)
 
-## Collaborate with your team
+The python file named read_cifar.py contains the functions needed to read the data. 
 
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
+The python file named knn.py contains the functions developping and testing the k-nearest neighbors classification model.
 
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+The python file named mlp.py contains the functions developping and testing the multilayer perceptron neural networks classification model.
 
 ***
 
-
 ## Author 
 Myla Arab