diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt
index 454c480ae..b60bdf733 100644
--- a/doc/src/week34/week34.do.txt
+++ b/doc/src/week34/week34.do.txt
@@ -582,8 +582,8 @@ use Python during our lectures and in various projects and exercises.
 Those of you
 already familiar with _R_ should feel free to continue using _R_, keeping
 however an eye on the parallel Python set ups. Similarly, if you are a
-Python afecionado, feel free to explore _R_ as well.  Jupyter/Ipython
-notebook allows you to run _R_ codes interactively in your
+Python afecionado, feel free to explore _R_ as well.  Jupyter(Julia, Python and R) /Ipython
+notebook allows you to run _R_ codes and _Julia_ codes interactively in your
 browser. The software library _R_ is really tailored  for statistical data analysis
 and allows for an easy usage of the tools and algorithms we will discuss in these
 lectures.
@@ -620,9 +620,7 @@ pycod jupyter nbconvert filename.ipynb --to latex
 
 And to add more versatility, the Python package "SymPy":"http://www.sympy.org/en/index.html" is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS)  and is entirely written in Python. 
 
-Finally, if you wish to use the light mark-up language 
-"doconce":"https://github.com/hplgit/doconce" you can convert a standard ascii text file into various HTML 
-formats, ipython notebooks, latex files, pdf files etc with minimal edits. These lectures were generated using _doconce_.
+Finally, we recommend strongly using Autograd or JAX for automatic differentiation.
 
 
 !split