Training the Variational Autoencoder After applying the reparameterization trick In a Variational Autoencoders, z cannot be simply sampled from the output of the autoencoder directly since the network would not be differentiable. Instead, we have to sample from a normal distribution who's parameters are the output of the autoencoder, mu and stddev. 

I So cannot understand how the network isn't differentiable (lets say we take z = mu + srd). So the reparameterization is not clear to me. 

Any help would be much appreciated!!
Have you read Doersch's tutorial on VAE? In paragraph 1.1 the author shows main intuitions behind the latent model and in the following sections he goes deeper into the explanation of how a VAE works. I think it's a good starting point to approach to VAEs.