diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/lessons/3-NeuralNetworks/04-OwnFramework/README.md
index 5afddc6a..f649500b 100644
--- a/lessons/3-NeuralNetworks/04-OwnFramework/README.md
+++ b/lessons/3-NeuralNetworks/04-OwnFramework/README.md
@@ -29,7 +29,7 @@ In the definition of *f* above, *w* and *b* are called **parameters** θ=
 
 ## Gradient Descent Optimization
 
-There is a well-known method of function optimization called **gradient descent**. The idea is that we can compute a derivative (in multi-dimensional case call **gradient**) of loss function with respect to parameters, and vary parameters in such a way that the error would decrease. This can be formalized as follows:
+There is a well-known method of function optimization called **gradient descent**. The idea is that we can compute a derivative (in multi-dimensional case called **gradient**) of loss function with respect to parameters, and vary parameters in such a way that the error would decrease. This can be formalized as follows:
 
 * Initialize parameters by some random values w<sup>(0)</sup>, b<sup>(0)</sup>
 * Repeat the following step many times: