diff --git a/3-NeuralNetworks/03-Perceptron/README.md b/3-NeuralNetworks/03-Perceptron/README.md
index 6ba59682..f9ea8dc9 100644
--- a/3-NeuralNetworks/03-Perceptron/README.md
+++ b/3-NeuralNetworks/03-Perceptron/README.md
@@ -60,6 +60,6 @@ def train(positive_examples, negative_examples, num_iterations = 100, eta = 1):
     return weights
 ```
 
-## Proceed in Notebook
+## [Proceed to Notebook](Perceptron.ipynb)
 
 To see how we can use perceptron to solve some toy as well as real-life problems, and to continue learning - go to [Perceptron](Perceptron.ipynb) notebook.
diff --git a/3-NeuralNetworks/README.md b/3-NeuralNetworks/README.md
index cb710a41..75fb6552 100644
--- a/3-NeuralNetworks/README.md
+++ b/3-NeuralNetworks/README.md
@@ -22,7 +22,7 @@ In this curricula, we will only focus on neural network models.
 
 From biology we know that our brain consists of neural cells, each of them having multiple "inputs" (axons), and an output (dendrite). Axons and dendrites can conduct electrical signals, and connections between axons and dendrites can exhibit different degrees of conductivity (controlled by neuromediators).
 
-![Model of a Neuron](images/synapse-wikipedia.JPG) | ![Model of a Neuron](images/artneuron.png)
+![Model of a Neuron](images/synapse-wikipedia.jpg) | ![Model of a Neuron](images/artneuron.png)
 ----|----
 Real Neuron | Artificial Neuron