Using-Fidle/Install-Fidle.md

@@ -16,7 +16,18 @@
 
 The **installation** consists of **3 steps**:
 
-[[_TOC_]]
+* [1 - Get Fidle repository](#1-get-fidle-repository)
+* [2 - Get and install Datasets](#2-get-and-install-datasets)
+* [3 - Setup Python Environment](#3-setup-python-environment)
+  * [I just have a CPU :](#i-just-have-a-cpu-)
+  * [I have a nice GPU :](#i-have-a-nice-gpu-)
+* [4 - Start Jupyter lab :](#4-start-jupyter-lab-)
+* [Annexe 1 - Manual installation](#annexe-1-manual-installation)
+  * [I just have a CPU :](#i-just-have-a-cpu--1)
+  * [I have a nice GPU :](#i-have-a-nice-gpu--1)
+* [Annexe 2 - Test installations](#annexe-2-test-installations)
+  * [Check Tensorflow / Keras](#check-tensorflow-keras)
+  * [Check Pytorch](#check-pytorch)
 
 ## 1 - Get Fidle repository
 
@@ -94,14 +105,12 @@ Very simply, as follows.
    Note : for Windows, use a Anaconda Prompt terminal to type these commands.
 3. **Start** Jupyter lab
 
-```
+```plaintext
 cd <PATH_TO_CLONED_FIDLE_REPO>
-conda activate fidle-cpu
+conda activate fidle
 jupyter lab
 ```
 
-Note: replace `fidle-cpu `by `fidle-gpu` if needed ;-)
-
 ## Annexe 1 - Manual installation
 
 This solution can be adapted if the procedure described above does not work.\
@@ -124,7 +133,7 @@ With conda you can use :
 
 * If no NVIDIA card is available on your computer or [NVIDIA card is not compliant with driver cuda 10.x](https://docs.nvidia.com/deploy/cuda-compatibility/index.html)
 
-```
+```plaintext
 conda create --name fidle 
 conda activate fidle
 conda install tensorflow>=2.4 numpy=1.19 keras scikit-learn scikit-image
@@ -167,6 +176,6 @@ print(torch.cuda.is_available())
 print(torch.cuda.device_count())
 ```
 
-L'ancienne procédure est [accessible ici :-)](Using-Fidle/install fidle v1)
+L'ancienne procédure est [accessible ici :-)](Using-Fidle/install%20fidle%20v1)
 
 ![](uploads/fd909e4607c0e9858a8088b5f39f60f5/00-fidle-ender-03.svg)
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