Update README.md

README.md

@@ -63,12 +63,16 @@ Some other useful informations are also available in the [wiki](https://gricad-g
       Episode 4 : Improving the results with data augmentation  
 [[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)  
       Episode 5 : A lot of models, a lot of datasets and a lot of results.  
-[[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)  
+[[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)  
       Episode 6 : Run Full convolution notebook as a batch  
-[[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb)  
+[[GTS7] - CNN with GTSRB dataset - Show reports](GTSRB/07-Show-report.ipynb)  
       Episode 7 : Displaying the reports of the different jobs  
 [[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)  
       4 ways to use Tensorboard from the Jupyter environment  
+[[BASH1] - OAR batch script](GTSRB/batch_oar.sh)  
+      Bash script for OAR batch submission of GTSRB notebook  
+[[BASH2] - SLURM batch script](GTSRB/batch_slurm.sh)  
+      Bash script for SLURM batch submission of GTSRB notebooks  
 [[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)  
       A very classical example of word embedding for text classification (sentiment analysis)  
 [[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)  
@@ -83,11 +87,13 @@ Some other useful informations are also available in the [wiki](https://gricad-g
       Episode 3: Attempt to predict in the longer term   
 [[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)  
       Episode 1 : Model construction and Training  
+[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.nbconvert.ipynb)  
+      Episode 1 : Model construction and Training  
 [[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)  
       Episode 2 : Exploring our latent space  
 [[VAE3] - About the CelebA dataset](VAE/03-About-CelebA.ipynb)  
       Episode 3 : About the CelebA dataset, a more fun dataset !  
-[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb)  
+[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-datasets.ipynb)  
       Episode 4 : Preparation of a clustered dataset, batchable  
 [[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb)  
       Episode 5 :\tChecking the clustered dataset  
@@ -97,10 +103,10 @@ Some other useful informations are also available in the [wiki](https://gricad-g
       Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.)  
 [[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb)  
       Episode 8 : Exploring latent space of our trained models  
-[[BASH1] - OAR batch script](VAE/batch-oar.sh)  
-      Bash script for OAR batch submission of a notebook  
-[[BASH2] - SLURM batch script](VAE/batch-slurm.sh)  
-      Bash script for SLURM batch submission of a notebook  
+[[BASH1] - OAR batch script](VAE/batch_oar.sh)  
+      Bash script for OAR batch submission of VAE notebook  
+[[BASH2] - SLURM batch script](VAE/batch_slurm.sh)  
+      Bash script for SLURM batch submission of VAE notebooks  
 [[ACTF1] - Activation functions](Misc/Activation-Functions.ipynb)  
       Some activation functions, with their derivatives.  
 [[NP1] - A short introduction to Numpy](Misc/Numpy.ipynb)