diff --git a/tutorials/03-advanced/variational_auto_encoder/README.md b/tutorials/03-advanced/variational_auto_encoder/README.md
index df7ded7..927ed13 100644
--- a/tutorials/03-advanced/variational_auto_encoder/README.md
+++ b/tutorials/03-advanced/variational_auto_encoder/README.md
@@ -1,4 +1,4 @@
-## Variational Auto-Encoder
+# Variational Auto-Encoder
 [Variational Auto-Encoder(VAE)](https://arxiv.org/abs/1312.6114) is one of the generative model. From a neural network perspective, the only difference between the VAE and the Auto-Encoder(AE) is that the latent vector z in VAE is stochastically sampled. This solves the problem that the AE learns identity mapping and can not have meaningful representations in latent space. In fact, the VAE uses [reparameterization trick](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/variational_auto_encoder/main.py#L40-L44) to enable back propagation without sampling z directly from the mean and variance.
 
 #### VAE loss
@@ -21,4 +21,4 @@ $ python main.py
 ## Results
 Real image        |  Reconstruced image
 :-------------------------:|:-------------------------:
-![alt text](png/real.png)  |  ![alt text](png/reconst.png)
\ No newline at end of file
+![alt text](png/real.png)  |  ![alt text](png/reconst.png)