diff --git a/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb b/DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb
similarity index 99%
rename from DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
rename to DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb
index ff676d8de18c94ea905ede53f87b0afc83ca7cb8..a4465693b164a5fef9d4b06baeb77f2adc8b8fa2 100644
--- a/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
+++ b/DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb
@@ -7,7 +7,7 @@
     "<img width=\"800px\" src=\"../fidle/img/header.svg\"></img>\n",
     "\n",
     "# <!-- TITLE --> [SHEEP1] - A first DCGAN to Draw a Sheep\n",
-    "<!-- DESC --> Episode 1 : Draw me a sheep, revisited with a DCGAN\n",
+    "<!-- DESC --> \"Draw me a sheep\", revisited with a DCGAN\n",
     "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
     "\n",
     "## Objectives :\n",
diff --git a/DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb b/DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb
similarity index 99%
rename from DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb
rename to DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb
index dee76ea54d0d623523957d016271db992b7b307a..5ff34437c32a6d1332cf892bdc7e0b6b3103c170 100644
--- a/DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb
+++ b/DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb
@@ -7,7 +7,7 @@
     "<img width=\"800px\" src=\"../fidle/img/header.svg\"></img>\n",
     "\n",
     "# <!-- TITLE --> [SHEEP2] - A WGAN-GP to Draw a Sheep\n",
-    "<!-- DESC --> Episode 2 : Draw me a sheep, revisited with a WGAN-GP\n",
+    "<!-- DESC --> \"Draw me a sheep\", revisited with a WGAN-GP\n",
     "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
     "\n",
     "## Objectives :\n",
diff --git a/DCGAN/modules/callbacks/ImagesCallback.py b/DCGAN.Keras/modules/callbacks/ImagesCallback.py
similarity index 100%
rename from DCGAN/modules/callbacks/ImagesCallback.py
rename to DCGAN.Keras/modules/callbacks/ImagesCallback.py
diff --git a/DCGAN/modules/callbacks/__init__.py b/DCGAN.Keras/modules/callbacks/__init__.py
similarity index 100%
rename from DCGAN/modules/callbacks/__init__.py
rename to DCGAN.Keras/modules/callbacks/__init__.py
diff --git a/DCGAN/modules/models/DCGAN.py b/DCGAN.Keras/modules/models/DCGAN.py
similarity index 100%
rename from DCGAN/modules/models/DCGAN.py
rename to DCGAN.Keras/modules/models/DCGAN.py
diff --git a/DCGAN/modules/models/WGANGP.py b/DCGAN.Keras/modules/models/WGANGP.py
similarity index 100%
rename from DCGAN/modules/models/WGANGP.py
rename to DCGAN.Keras/modules/models/WGANGP.py
diff --git a/DCGAN/modules/models/__init__.py b/DCGAN.Keras/modules/models/__init__.py
similarity index 100%
rename from DCGAN/modules/models/__init__.py
rename to DCGAN.Keras/modules/models/__init__.py
diff --git a/DCGAN-PyTorch/01-DCGAN-PL.ipynb b/DCGAN.Lightning/01-DCGAN-PL.ipynb
similarity index 98%
rename from DCGAN-PyTorch/01-DCGAN-PL.ipynb
rename to DCGAN.Lightning/01-DCGAN-PL.ipynb
index fb2912157113d067211ba2fa8969a2e68bf88992..98d335e518c8091d2b97f072e3793c1f03dd550d 100644
--- a/DCGAN-PyTorch/01-DCGAN-PL.ipynb
+++ b/DCGAN.Lightning/01-DCGAN-PL.ipynb
@@ -6,8 +6,8 @@
    "source": [
     "<img width=\"800px\" src=\"../fidle/img/header.svg\"></img>\n",
     "\n",
-    "# <!-- TITLE --> [SHEEP3] - A DCGAN to Draw a Sheep, with Pytorch Lightning\n",
-    "<!-- DESC --> Episode 1 : Draw me a sheep, revisited with a DCGAN, writing in Pytorch Lightning\n",
+    "# <!-- TITLE --> [SHEEP3] - A DCGAN to Draw a Sheep, using Pytorch Lightning\n",
+    "<!-- DESC --> \"Draw me a sheep\", revisited with a DCGAN, using Pytorch Lightning\n",
     "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
     "\n",
     "## Objectives :\n",
diff --git a/DCGAN-PyTorch/modules/Discriminators.py b/DCGAN.Lightning/modules/Discriminators.py
similarity index 100%
rename from DCGAN-PyTorch/modules/Discriminators.py
rename to DCGAN.Lightning/modules/Discriminators.py
diff --git a/DCGAN-PyTorch/modules/GAN.py b/DCGAN.Lightning/modules/GAN.py
similarity index 100%
rename from DCGAN-PyTorch/modules/GAN.py
rename to DCGAN.Lightning/modules/GAN.py
diff --git a/DCGAN-PyTorch/modules/Generators.py b/DCGAN.Lightning/modules/Generators.py
similarity index 100%
rename from DCGAN-PyTorch/modules/Generators.py
rename to DCGAN.Lightning/modules/Generators.py
diff --git a/DCGAN-PyTorch/modules/QuickDrawDataModule.py b/DCGAN.Lightning/modules/QuickDrawDataModule.py
similarity index 100%
rename from DCGAN-PyTorch/modules/QuickDrawDataModule.py
rename to DCGAN.Lightning/modules/QuickDrawDataModule.py
diff --git a/DCGAN-PyTorch/modules/SmartProgressBar.py b/DCGAN.Lightning/modules/SmartProgressBar.py
similarity index 100%
rename from DCGAN-PyTorch/modules/SmartProgressBar.py
rename to DCGAN.Lightning/modules/SmartProgressBar.py
diff --git a/DCGAN-PyTorch/modules/WGANGP.py b/DCGAN.Lightning/modules/WGANGP.py
similarity index 100%
rename from DCGAN-PyTorch/modules/WGANGP.py
rename to DCGAN.Lightning/modules/WGANGP.py
diff --git a/README.ipynb b/README.ipynb
index 18f0ab1cb3bb79491403f83b3edc43e6263f89c0..37db95df9687f02cf652333dc78fd963be486bb8 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c7f25450",
+   "id": "173d25d2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-31T17:24:12.865964Z",
-     "iopub.status.busy": "2023-10-31T17:24:12.865178Z",
-     "iopub.status.idle": "2023-10-31T17:24:12.875759Z",
-     "shell.execute_reply": "2023-10-31T17:24:12.874784Z"
+     "iopub.execute_input": "2023-11-06T14:13:54.813170Z",
+     "iopub.status.busy": "2023-11-06T14:13:54.812440Z",
+     "iopub.status.idle": "2023-11-06T14:13:54.823230Z",
+     "shell.execute_reply": "2023-11-06T14:13:54.822337Z"
     },
     "jupyter": {
      "source_hidden": true
@@ -52,7 +52,7 @@
        "For more information, you can contact us at :  \n",
        "[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)\n",
        "\n",
-       "Current Version : <!-- VERSION_BEGIN -->2.4.0<!-- VERSION_END -->\n",
+       "Current Version : <!-- VERSION_BEGIN -->2.4.1<!-- VERSION_END -->\n",
        "\n",
        "\n",
        "## Course materials\n",
@@ -67,7 +67,7 @@
        "## Jupyter notebooks\n",
        "\n",
        "<!-- TOC_BEGIN -->\n",
-       "<!-- Automatically generated on : 31/10/23 18:24:11 -->\n",
+       "<!-- Automatically generated on : 06/11/23 15:13:53 -->\n",
        "\n",
        "### Linear and logistic regression\n",
        "- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)  \n",
@@ -98,7 +98,7 @@
        "Another example of regression, with a wine quality prediction!\n",
        "\n",
        "### Wine Quality prediction (DNN), using PyTorch\n",
-       "- **[LWINE1](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)** - [Wine quality prediction with a Dense Network (DNN) using Lightning](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)  \n",
+       "- **[WINE1](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)** - [Wine quality prediction with a Dense Network (DNN) using Lightning](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)  \n",
        "Another example of regression, with a wine quality prediction!\n",
        "\n",
        "### MNIST classification (DNN,CNN), using Keras\n",
@@ -112,9 +112,9 @@
        "Example of classification with a fully connected neural network, using Pytorch\n",
        "\n",
        "### MNIST classification (DNN,CNN), using Lightning\n",
-       "- **[LMNIST1](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)** - [Simple classification with DNN using Pytorch Lightning](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)  \n",
+       "- **[MNIST2](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)** - [Simple classification with DNN using pytorch lightning](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)  \n",
        "An example of classification using a dense neural network for the famous MNIST dataset\n",
-       "- **[LMNIST2](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)** - [Simple classification with CNN using Pytorch Lightning](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)  \n",
+       "- **[MNIST2](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)** - [Simple classification with CNN using lightning](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)  \n",
        "An example of classification using a convolutional neural network for the famous MNIST dataset\n",
        "\n",
        "### Images classification with Convolutional Neural Networks (CNN)\n",
@@ -185,11 +185,15 @@
        "- **[VAE3](VAE/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE/03-VAE-with-MNIST-post.ipynb)  \n",
        "Visualization and analysis of the VAE's latent space of the dataset MNIST\n",
        "\n",
-       "### Generative Adversarial Networks (GANs)\n",
-       "- **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)  \n",
-       "Episode 1 : Draw me a sheep, revisited with a DCGAN\n",
-       "- **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)  \n",
-       "Episode 2 : Draw me a sheep, revisited with a WGAN-GP\n",
+       "### Generative Adversarial Networks (GANs), using Keras\n",
+       "- **[SHEEP1](DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb)  \n",
+       "\"Draw me a sheep\", revisited with a DCGAN\n",
+       "- **[SHEEP2](DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb)  \n",
+       "\"Draw me a sheep\", revisited with a WGAN-GP\n",
+       "\n",
+       "### Generative Adversarial Networks (GANs), using Lightning\n",
+       "- **[SHEEP3](DCGAN.Lightning/01-DCGAN-PL.ipynb)** - [A DCGAN to Draw a Sheep, using Pytorch Lightning](DCGAN.Lightning/01-DCGAN-PL.ipynb)  \n",
+       "\"Draw me a sheep\", revisited with a DCGAN, using Pytorch Lightning\n",
        "\n",
        "### Diffusion Model (DDPM)\n",
        "- **[DDPM1](DDPM/01-ddpm.ipynb)** - [Fashion MNIST Generation with DDPM](DDPM/01-ddpm.ipynb)  \n",
@@ -251,7 +255,7 @@
     "from IPython.display import display,Markdown\n",
     "display(Markdown(open('README.md', 'r').read()))\n",
     "#\n",
-    "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 31/10/23 18:24:11"
+    "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 06/11/23 15:13:53"
    ]
   }
  ],
diff --git a/README.md b/README.md
index 09730272b709a37cfcc53ad3ccfa053f9ea4aba7..93c753535c308b5240dd1287918abd31b05ed1c5 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ For more information, see **https://fidle.cnrs.fr** :
 For more information, you can contact us at :  
 [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
 
-Current Version : <!-- VERSION_BEGIN -->2.4.0<!-- VERSION_END -->
+Current Version : <!-- VERSION_BEGIN -->2.4.1<!-- VERSION_END -->
 
 
 ## Course materials
@@ -46,7 +46,7 @@ Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)
 ## Jupyter notebooks
 
 <!-- TOC_BEGIN -->
-<!-- Automatically generated on : 31/10/23 18:24:11 -->
+<!-- Automatically generated on : 06/11/23 15:13:53 -->
 
 ### Linear and logistic regression
 - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)  
@@ -77,7 +77,7 @@ A Simple regression with a Dense Neural Network (DNN) using Pytorch - BHPD datas
 Another example of regression, with a wine quality prediction!
 
 ### Wine Quality prediction (DNN), using PyTorch
-- **[LWINE1](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)** - [Wine quality prediction with a Dense Network (DNN) using Lightning](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)  
+- **[WINE1](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)** - [Wine quality prediction with a Dense Network (DNN) using Lightning](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)  
 Another example of regression, with a wine quality prediction!
 
 ### MNIST classification (DNN,CNN), using Keras
@@ -91,9 +91,9 @@ An example of classification using a convolutional neural network for the famous
 Example of classification with a fully connected neural network, using Pytorch
 
 ### MNIST classification (DNN,CNN), using Lightning
-- **[LMNIST1](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)** - [Simple classification with DNN using Pytorch Lightning](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)  
+- **[MNIST2](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)** - [Simple classification with DNN using pytorch lightning](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)  
 An example of classification using a dense neural network for the famous MNIST dataset
-- **[LMNIST2](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)** - [Simple classification with CNN using Pytorch Lightning](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)  
+- **[MNIST2](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)** - [Simple classification with CNN using lightning](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)  
 An example of classification using a convolutional neural network for the famous MNIST dataset
 
 ### Images classification with Convolutional Neural Networks (CNN)
@@ -164,11 +164,15 @@ Construction and training of a VAE, using model subclass, with a latent space of
 - **[VAE3](VAE/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE/03-VAE-with-MNIST-post.ipynb)  
 Visualization and analysis of the VAE's latent space of the dataset MNIST
 
-### Generative Adversarial Networks (GANs)
-- **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)  
-Episode 1 : Draw me a sheep, revisited with a DCGAN
-- **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)  
-Episode 2 : Draw me a sheep, revisited with a WGAN-GP
+### Generative Adversarial Networks (GANs), using Keras
+- **[SHEEP1](DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb)  
+"Draw me a sheep", revisited with a DCGAN
+- **[SHEEP2](DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb)  
+"Draw me a sheep", revisited with a WGAN-GP
+
+### Generative Adversarial Networks (GANs), using Lightning
+- **[SHEEP3](DCGAN.Lightning/01-DCGAN-PL.ipynb)** - [A DCGAN to Draw a Sheep, using Pytorch Lightning](DCGAN.Lightning/01-DCGAN-PL.ipynb)  
+"Draw me a sheep", revisited with a DCGAN, using Pytorch Lightning
 
 ### Diffusion Model (DDPM)
 - **[DDPM1](DDPM/01-ddpm.ipynb)** - [Fashion MNIST Generation with DDPM](DDPM/01-ddpm.ipynb)  
diff --git a/fidle/about.yml b/fidle/about.yml
index 32032a38ed6e409170f3d7173f34f3aaebfa1e35..da3528c0e3ade98206ad3da72e8b4fa70712543f 100644
--- a/fidle/about.yml
+++ b/fidle/about.yml
@@ -13,7 +13,7 @@
 #
 # This file describes the notebooks used by the Fidle training.
 
-version:          2.4.0
+version:          2.4.1
 content:          notebooks
 name:             Notebooks Fidle
 description:      All notebooks used by the Fidle training
@@ -39,7 +39,8 @@ toc:
   Transformers:    Sentiment analysis with transformer
   AE:              Unsupervised learning with an autoencoder neural network (AE)
   VAE:             Generative network with Variational Autoencoder (VAE)
-  DCGAN:           Generative Adversarial Networks (GANs)
+  DCGAN.Keras:     Generative Adversarial Networks (GANs), using Keras
+  DCGAN.Lightning: Generative Adversarial Networks (GANs), using Lightning
   DDPM:            Diffusion Model (DDPM)
   Optimization:    Training optimization
   DRL:             Deep Reinforcement Learning (DRL)
diff --git a/fidle/ci/default.yml b/fidle/ci/default.yml
index 372cef3ebc2ef0e995d9dc66e0c37d7c8a65cdb7..a89237d69437931b77a2125045c28404e1fae3b8 100644
--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
@@ -1,6 +1,6 @@
 campain:
   version: '1.0'
-  description: Automatically generated ci profile (31/10/23 18:24:11)
+  description: Automatically generated ci profile (06/11/23 15:13:53)
   directory: ./campains/default
   existing_notebook: 'remove    # remove|skip'
   report_template: 'fidle     # fidle|default'
@@ -54,7 +54,7 @@ KWINE1:
 #
 # ------------ Wine.Lightning
 #
-LWINE1:
+WINE1:
   notebook: Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb
   overrides:
     fit_verbosity: default
@@ -81,9 +81,7 @@ PMNIST1:
 #
 # ------------ MNIST.Lightning
 #
-LMNIST1:
-  notebook: MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb
-LMNIST2:
+MNIST2:
   notebook: MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
 
 #
@@ -310,10 +308,10 @@ VAE3:
     models_dir: default
 
 #
-# ------------ DCGAN
+# ------------ DCGAN.Keras
 #
 SHEEP1:
-  notebook: DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
+  notebook: DCGAN.Keras/01-DCGAN-Draw-me-a-sheep.ipynb
   overrides:
     scale: default
     latent_dim: default
@@ -322,7 +320,7 @@ SHEEP1:
     num_img: default
     fit_verbosity: default
 SHEEP2:
-  notebook: DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb
+  notebook: DCGAN.Keras/02-WGANGP-Draw-me-a-sheep.ipynb
   overrides:
     scale: default
     latent_dim: default
@@ -331,6 +329,12 @@ SHEEP2:
     num_img: default
     fit_verbosity: default
 
+#
+# ------------ DCGAN.Lightning
+#
+SHEEP3:
+  notebook: DCGAN.Lightning/01-DCGAN-PL.ipynb
+
 #
 # ------------ DDPM
 #