diff --git a/README.md b/README.md
index 2783fa2884226e1354a22e260936dda74f13451a..8ce7a5653f0b9396272cb6cf61a476f6286563d1 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@ We recommand to use the notebook (.ipynb) but the Python script (.py) is also pr
 
 The purpose of this task is to explore Generative Adversarial Networks (GANs) and their implementation, specifically focusing on an architecture that enables image-to-image translation. 
 
-# Fake numbers generations
+# Fake numbers generation
 
  In the first part, we aim to learn and understand the basic concepts of Generative Adversarial Networks through a DCGAN and generate new handwritten numbers from the learned network after showing it real handwritten numbers.