diff --git a/prep/vae.md b/prep/vae.md
index 0a11394..215b751 100644
--- a/prep/vae.md
+++ b/prep/vae.md
@@ -1,13 +1,11 @@
-## Preparations for lecture on Variational Auto Encoders
+## Preparations for lecture on Auto Encoders
 
-As a preparation for the Variational Auto Encoders convocation, make yourself acquainted with the material below, and subsequently complete any tasks assigned to you in canvas.  
-*Note that this is an advanced topic and you will not be required to get all the details of the material*.
+As a preparation for the Auto Encoders convocation, make yourself acquainted with the material below, and subsequently complete any tasks assigned to you in canvas.  
 
-1. Watch the [Video Lecture on Variational Auto Encoders](https://youtu.be/dPRPGA0krOs), and its [slides](slides/VariationalAutoEncoders.pdf)
-2. Read the Blog post on [VAE](https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73)
-3. Have a look at the example code in the jupyter [notebook](../nb/vae/)
+1. Read the chapters on [Auto Encoders](https://www.kaell.se/dsbook/unsupervised/autoenc.html) and its [example implementation](https://www.kaell.se/dsbook/unsupervised/VAEofCarcinomas.html) 
 
 ## Additional material
 
-* If you want more details, on VAE, the original [preprint](https://arxiv.org/pdf/1312.6114.pdf) is nice.
-* A bit out of the context, but still relevant for the lecture, this [tool](https://playground.tensorflow.org/) illustrate the effects of architecture and regularization schemes have when training a ANN for different dataset. 
+* A nice Blog post on [VAE](https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73)
+
+* If you want more details, on VAE, the original [preprint](https://arxiv.org/pdf/1312.6114.pdf) is nice, but might be challenging.
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