Update to 3.0.10

527fd716 · Jean-Luc Parouty · 9dbbb73d · 527fd716 · 527fd716 · 527fd716
Commit 527fd716 authored 1 year ago by Jean-Luc Parouty
--- a/Misc/06-Gradients.ipynb
+++ b/Misc/06-Gradients.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pure Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "f(1) is :  0\n",
+      "f(2) is :  7\n",
+      "df(3) is :  10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- My basic function f\n",
+    "def f(x):\n",
+    "    y = x*x + 4*x - 5\n",
+    "    return y\n",
+    "\n",
+    "def df(x):\n",
+    "    y=2*x + 4\n",
+    "    return y\n",
+    "\n",
+    "# ---- Examples :\n",
+    "print('f(1) is : ', f(1))\n",
+    "print('f(2) is : ', f(2))\n",
+    "\n",
+    "print('df(3) is : ',df(3))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get a nice tensor, with `requires_grad=True` :-)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x: tensor(3., requires_grad=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = torch.tensor(3.0, requires_grad = True)\n",
+    "print(\"x:\", x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define our function.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = x*x + 4*x + - 5 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute gradient with the backward function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dx= tensor(10.)\n"
+     ]
+    }
+   ],
+   "source": [
+    "dx=x.grad\n",
+    "print('dx=',dx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fidle-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:code id: tags:
+
+``` python
+import torch
+```
+
+%% Cell type:markdown id: tags:
+
+## Pure Python
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- My basic function f
+def f(x):
+    y = x*x + 4*x - 5
+    return y
+
+def df(x):
+    y=2*x + 4
+    return y
+
+# ---- Examples :
+print('f(1) is : ', f(1))
+print('f(2) is : ', f(2))
+
+print('df(3) is : ',df(3))
+```
+
+%% Output
+
+    f(1) is :  0
+    f(2) is :  7
+    df(3) is :  10
+
+%% Cell type:markdown id: tags:
+
+## Using Torch
+
+%% Cell type:markdown id: tags:
+
+Get a nice tensor, with `requires_grad=True` :-)
+
+%% Cell type:code id: tags:
+
+``` python
+x = torch.tensor(3.0, requires_grad = True)
+print("x:", x)
+```
+
+%% Output
+
+    x: tensor(3., requires_grad=True)
+
+%% Cell type:markdown id: tags:
+
+Define our function..
+
+%% Cell type:code id: tags:
+
+``` python
+y = x*x + 4*x + - 5
+```
+
+%% Cell type:markdown id: tags:
+
+Compute gradient with the backward function
+
+%% Cell type:code id: tags:
+
+``` python
+y.backward()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+dx=x.grad
+print('dx=',dx)
+```
+
+%% Output
+
+    dx= tensor(10.)
+
+%% Cell type:code id: tags:
+
+``` python
+```
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
  {
   "cell_type": "code",
   "execution_count": 1,
-   "id": "60a4d6df",
+   "id": "3f8d27e1",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2024-03-03T19:38:38.844681Z",
-     "iopub.status.busy": "2024-03-03T19:38:38.844023Z",
-     "iopub.status.idle": "2024-03-03T19:38:38.854300Z",
-     "shell.execute_reply": "2024-03-03T19:38:38.853434Z"
+     "iopub.execute_input": "2024-03-20T20:58:24.985713Z",
+     "iopub.status.busy": "2024-03-20T20:58:24.985441Z",
+     "iopub.status.idle": "2024-03-20T20:58:24.998771Z",
+     "shell.execute_reply": "2024-03-20T20:58:24.997910Z"
    },
    "jupyter": {
     "source_hidden": true
@@ -53,14 +53,14 @@
       "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 -->3.0.9<!-- VERSION_END -->\n",
+       "Current Version : <!-- VERSION_BEGIN -->3.0.10<!-- VERSION_END -->\n",
       "\n",
       "\n",
       "## Course materials\n",
       "\n",
-       "| | | | |\n",
+       "| Courses | Notebooks | Datasets | Videos |\n",
       "|:--:|:--:|:--:|:--:|\n",
-       "| **[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>| **[<img width=\"50px\" src=\"fidle/img/00-Notebooks.svg\"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>| **[<img width=\"50px\" src=\"fidle/img/00-Datasets-tar.svg\"></img><br>Datasets](https://fidle.cnrs.fr/datasets-fidle.tar)**<br>All the needed datasets<br>|**[<img width=\"50px\" src=\"fidle/img/00-Videos.svg\"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|\n",
+       "| [<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img><br>**Course slides**](https://fidle.cnrs.fr/supports)<br>The course in pdf format<br>| [<img width=\"50px\" src=\"fidle/img/00-Notebooks.svg\"></img><br>**Notebooks**](https://fidle.cnrs.fr/notebooks)<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>| [<img width=\"50px\" src=\"fidle/img/00-Datasets-tar.svg\"></img><br>**Datasets**](https://fidle.cnrs.fr/datasets-fidle.tar)<br>All the needed datasets<br>|[<img width=\"50px\" src=\"fidle/img/00-Videos.svg\"></img><br>**Videos**](https://fidle.cnrs.fr/youtube)<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|\n",
       "\n",
       "Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.\n",
       "\n",
@@ -68,7 +68,7 @@
       "## Jupyter notebooks\n",
       "\n",
       "<!-- TOC_BEGIN -->\n",
-       "<!-- Automatically generated on : 03/03/24 20:38:37 -->\n",
+       "<!-- Automatically generated on : 20/03/24 21:58:23 -->\n",
       "\n",
       "### Linear and logistic regression\n",
       "- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)  \n",
@@ -207,6 +207,8 @@
       "4 ways to use Tensorboard from the Jupyter environment\n",
       "- **[??](Misc/05-RNN.ipynb)** - [??](Misc/05-RNN.ipynb)  \n",
       "??\n",
+       "- **[??](Misc/06-Gradients.ipynb)** - [??](Misc/06-Gradients.ipynb)  \n",
+       "??\n",
       "<!-- TOC_END -->\n",
       "\n",
       "## Installation\n",
@@ -237,7 +239,7 @@
    "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 : 03/03/24 20:38:37"
+    "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 20/03/24 21:58:24"
   ]
  }
 ],

-%% Cell type:code id:60a4d6df tags:
+%% Cell type:code id:3f8d27e1 tags:

 ``` python
 from IPython.display import display,Markdown
 display(Markdown(open('README.md', 'r').read()))
 #
-# This README is visible under Jupiter Lab ;-)# Automatically generated on : 03/03/24 20:38:37
+# This README is visible under Jupiter Lab ;-)# Automatically generated on : 20/03/24 21:58:24
 ```

 %% Output

    <a name="top"></a>
    
    [<img width="600px" src="fidle/img/title.svg"></img>](#top)
    
    <!-- --------------------------------------------------- -->
    <!-- To correctly view this README under Jupyter Lab     -->
    <!-- Open the notebook: README.ipynb!                    -->
    <!-- --------------------------------------------------- -->
    
    ## About Fidle
    
    This repository contains all the documents and links of the **Fidle Training** .
    Fidle (for Formation Introduction au Deep Learning) is a 3-day training session co-organized
    by the 3IA MIAI institute, the CNRS, via the Mission for Transversal and Interdisciplinary
    Initiatives (MITI) and the University of Grenoble Alpes (UGA).
    
    The objectives of this training are :
     - Understanding the **bases of Deep Learning** neural networks
     - Develop a **first experience** through simple and representative examples
     - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
     - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
    
    For more information, see **https://fidle.cnrs.fr** :
    - **[Fidle site](https://fidle.cnrs.fr)**
    - **[Presentation of the training](https://fidle.cnrs.fr/presentation)**
    - **[Detailed program](https://fidle.cnrs.fr/programme)**
    - **[Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !**
    - **[Corrected notebooks](https://fidle.cnrs.fr/done)**
    - **[Follow us on our channel :](https://fidle.cnrs.fr/youtube)**\
    [<img width="120px" style="vertical-align:middle" src="fidle/img/logo-YouTube.png"></img>](https://fidle.cnrs.fr/youtube)
    
    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 -->3.0.9<!-- VERSION_END -->
+    Current Version : <!-- VERSION_BEGIN -->3.0.10<!-- VERSION_END -->
    
    
    ## Course materials
    
-    | | | | |
+    | Courses | Notebooks | Datasets | Videos |
    |:--:|:--:|:--:|:--:|
-    | **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/datasets-fidle.tar)**<br>All the needed datasets<br>|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|
+    | [<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>**Course slides**](https://fidle.cnrs.fr/supports)<br>The course in pdf format<br>| [<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>**Notebooks**](https://fidle.cnrs.fr/notebooks)<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>| [<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>**Datasets**](https://fidle.cnrs.fr/datasets-fidle.tar)<br>All the needed datasets<br>|[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>**Videos**](https://fidle.cnrs.fr/youtube)<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|
    
    Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
    
    
    ## Jupyter notebooks
    
    <!-- TOC_BEGIN -->
-    <!-- Automatically generated on : 03/03/24 20:38:37 -->
+    <!-- Automatically generated on : 20/03/24 21:58:23 -->
    
    ### Linear and logistic regression
    - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
    Low-level implementation, using numpy, of a direct resolution for a linear regression
    - **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
    Low level implementation of a solution by gradient descent. Basic and stochastic approach.
    - **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
    Illustration of the problem of complexity with the polynomial regression
    - **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb)
    Simple example of logistic regression with a sklearn solution
    
    ### Perceptron Model 1957
    - **[PER57](Perceptron/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](Perceptron/01-Simple-Perceptron.ipynb)
    Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !
    
    ### BHPD regression (DNN), using Keras3/PyTorch
    - **[K3BHPD1](BHPD.Keras3/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD.Keras3/01-DNN-Regression.ipynb)
    Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
    - **[K3BHPD2](BHPD.Keras3/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD.Keras3/02-DNN-Regression-Premium.ipynb)
    A more advanced implementation of the precedent example, using Keras3
    
    ### BHPD regression (DNN), using PyTorch
    - **[PBHPD1](BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb)** - [Regression with a Dense Network (DNN)](BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb)
    A Simple regression with a Dense Neural Network (DNN) using Pytorch - BHPD dataset
    
    ### Wine Quality prediction (DNN), using Keras3/PyTorch
    - **[K3WINE1](Wine.Keras3/01-DNN-Wine-Regression.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](Wine.Keras3/01-DNN-Wine-Regression.ipynb)
    Another example of regression, with a wine quality prediction, using Keras 3 and PyTorch
    
    ### Wine Quality prediction (DNN), using PyTorch/Lightning
    - **[LWINE1](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)
    Another example of regression, with a wine quality prediction, using PyTorch Lightning
    
    ### MNIST classification (DNN,CNN), using Keras3/PyTorch
    - **[K3MNIST1](MNIST.Keras3/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST.Keras3/01-DNN-MNIST.ipynb)
    An example of classification using a dense neural network for the famous MNIST dataset
    - **[K3MNIST2](MNIST.Keras3/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST.Keras3/02-CNN-MNIST.ipynb)
    An example of classification using a convolutional neural network for the famous MNIST dataset
    
    ### MNIST classification (DNN,CNN), using PyTorch
    - **[PMNIST1](MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb)** - [Simple classification with DNN](MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb)
    Example of classification with a fully connected neural network, using Pytorch
    
    ### MNIST classification (DNN,CNN), using PyTorch/Lightning
    - **[LMNIST1](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)** - [Simple classification with DNN](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)
    An example of classification using a dense neural network for the famous MNIST dataset, using PyTorch Lightning
    - **[LMNIST2](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)** - [Simple classification with CNN](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)
    An example of classification using a convolutional neural network for the famous MNIST dataset, using PyTorch Lightning
    
    ### Images classification GTSRB with Convolutional Neural Networks (CNN), using Keras3/PyTorch
    - **[K3GTSRB1](GTSRB.Keras3/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB.Keras3/01-Preparation-of-data.ipynb)
    Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
    - **[K3GTSRB2](GTSRB.Keras3/02-First-convolutions.ipynb)** - [First convolutions](GTSRB.Keras3/02-First-convolutions.ipynb)
    Episode 2 : First convolutions and first classification of our traffic signs, using Keras3
    - **[K3GTSRB3](GTSRB.Keras3/03-Better-convolutions.ipynb)** - [Training monitoring](GTSRB.Keras3/03-Better-convolutions.ipynb)
    Episode 3 : Monitoring, analysis and check points during a training session, using Keras3
    - **[K3GTSRB4](GTSRB.Keras3/04-Keras-cv.ipynb)** - [Hight level example (Keras-cv)](GTSRB.Keras3/04-Keras-cv.ipynb)
    An example of using a pre-trained model with Keras-cv
    - **[K3GTSRB10](GTSRB.Keras3/batch_oar.sh)** - [OAR batch script submission](GTSRB.Keras3/batch_oar.sh)
    Bash script for an OAR batch submission of an ipython code
    - **[K3GTSRB11](GTSRB.Keras3/batch_slurm.sh)** - [SLURM batch script](GTSRB.Keras3/batch_slurm.sh)
    Bash script for a Slurm batch submission of an ipython code
    
    ### Sentiment analysis with word embedding, using Keras3/PyTorch
    - **[K3IMDB1](Embedding.Keras3/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](Embedding.Keras3/01-One-hot-encoding.ipynb)
    A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB), using Keras 3 on PyTorch
    - **[K3IMDB2](Embedding.Keras3/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](Embedding.Keras3/02-Keras-embedding.ipynb)
    A very classical example of word embedding with a dataset from Internet Movie Database (IMDB), using Keras 3 on PyTorch
    - **[K3IMDB3](Embedding.Keras3/03-Prediction.ipynb)** - [Reload and reuse a saved model](Embedding.Keras3/03-Prediction.ipynb)
    Retrieving a saved model to perform a sentiment analysis (movie review), using Keras 3 and PyTorch
    - **[K3IMDB4](Embedding.Keras3/04-Show-vectors.ipynb)** - [Reload embedded vectors](Embedding.Keras3/04-Show-vectors.ipynb)
    Retrieving embedded vectors from our trained model, using Keras 3 and PyTorch
    - **[K3IMDB5](Embedding.Keras3/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](Embedding.Keras3/05-LSTM-Keras.ipynb)
    Still the same problem, but with a network combining embedding and RNN, using Keras 3 and PyTorch
    
    ### Time series with Recurrent Neural Network (RNN), using Keras3/PyTorch
    - **[K3LADYB1](RNN.Keras3/01-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](RNN.Keras3/01-Ladybug.ipynb)
    Artificial dataset generation and prediction attempt via a recurrent network, using Keras 3 and PyTorch
    
    ### Sentiment analysis with transformer, using PyTorch
    - **[TRANS1](Transformers.PyTorch/01-Distilbert.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers.PyTorch/01-Distilbert.ipynb)
    Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version
    - **[TRANS2](Transformers.PyTorch/02-distilbert_colab.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers.PyTorch/02-distilbert_colab.ipynb)
    Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version
    
    ### Unsupervised learning with an autoencoder neural network (AE), using Keras3
    - **[K3AE1](AE.Keras3/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE.Keras3/01-Prepare-MNIST-dataset.ipynb)
    Episode 1: Preparation of a noisy MNIST dataset
    - **[K3AE2](AE.Keras3/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE.Keras3/02-AE-with-MNIST.ipynb)
    Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.
    - **[K3AE3](AE.Keras3/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE.Keras3/03-AE-with-MNIST-post.ipynb)
    Episode 2 : Using the previously trained autoencoder to denoise data
    - **[K3AE4](AE.Keras3/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE.Keras3/04-ExtAE-with-MNIST.ipynb)
    Episode 4 : Construction of a denoiser and classifier model
    - **[K3AE5](AE.Keras3/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE.Keras3/05-ExtAE-with-MNIST.ipynb)
    Episode 5 : Construction of an advanced denoiser and classifier model
    
    ### Generative network with Variational Autoencoder (VAE), using Keras3
    - **[K3VAE1](VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb)** - [First VAE, using functional API (MNIST dataset)](VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb)
    Construction and training of a VAE, using functional APPI, with a latent space of small dimension.
    - **[K3VAE2](VAE.Keras3/02-VAE-with-MNIST.ipynb)** - [VAE, using a custom model class  (MNIST dataset)](VAE.Keras3/02-VAE-with-MNIST.ipynb)
    Construction and training of a VAE, using model subclass, with a latent space of small dimension.
    - **[K3VAE3](VAE.Keras3/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE.Keras3/03-VAE-with-MNIST-post.ipynb)
    Visualization and analysis of the VAE's latent space of the dataset MNIST
    
    ### Generative Adversarial Networks (GANs), using Lightning
    - **[PLSHEEP3](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) using PyTorch
    - **[DDPM1](DDPM.PyTorch/01-ddpm.ipynb)** - [Fashion MNIST Generation with DDPM](DDPM.PyTorch/01-ddpm.ipynb)
    Diffusion Model example, to generate Fashion MNIST images.
    - **[DDPM2](DDPM.PyTorch/model.py)** - [DDPM Python classes](DDPM.PyTorch/model.py)
    Python classes used by DDMP Example
    
    ### Training optimization, using PyTorch
    - **[OPT1](Optimization.PyTorch/01-Apprentissages-rapides-et-Optimisations.ipynb)** - [Training setup optimization](Optimization.PyTorch/01-Apprentissages-rapides-et-Optimisations.ipynb)
    The goal of this notebook is to go through a typical deep learning model training
    
    ### Deep Reinforcement Learning (DRL), using PyTorch
    - **[DRL1](DRL.PyTorch/FIDLE_DQNfromScratch.ipynb)** - [Solving CartPole with DQN](DRL.PyTorch/FIDLE_DQNfromScratch.ipynb)
    Using a a Deep Q-Network to play CartPole - an inverted pendulum problem (PyTorch)
    - **[DRL2](DRL.PyTorch/FIDLE_rl_baselines_zoo.ipynb)** - [RL Baselines3 Zoo: Training in Colab](DRL.PyTorch/FIDLE_rl_baselines_zoo.ipynb)
    Demo of Stable baseline3 with Colab
    
    ### Miscellaneous things, but very important!
    - **[NP1](Misc/00-Numpy.ipynb)** - [A short introduction to Numpy](Misc/00-Numpy.ipynb)
    Numpy is an essential tool for the Scientific Python.
    - **[ACTF1](Misc/01-Activation-Functions.ipynb)** - [Activation functions](Misc/01-Activation-Functions.ipynb)
    Some activation functions, with their derivatives.
    - **[PANDAS1](Misc/02-Using-pandas.ipynb)** - [Quelques exemples avec Pandas](Misc/02-Using-pandas.ipynb)
    pandas is another essential tool for the Scientific Python.
    - **[PYTORCH1](Misc/03-Using-Pytorch.ipynb)** - [Practical Lab : PyTorch](Misc/03-Using-Pytorch.ipynb)
    PyTorch est l'un des principaux framework utilisé dans le Deep Learning
    - **[TSB1](Misc/04-Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/04-Using-Tensorboard.ipynb)
    4 ways to use Tensorboard from the Jupyter environment
    - **[??](Misc/05-RNN.ipynb)** - [??](Misc/05-RNN.ipynb)
    ??
+    - **[??](Misc/06-Gradients.ipynb)** - [??](Misc/06-Gradients.ipynb)
+    ??
    <!-- TOC_END -->
    
    ## Installation
    
    Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
    
    ## Licence
    
    [<img width="100px" src="fidle/img/00-fidle-CC BY-NC-SA.svg"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/)
    \[en\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)
    \[Fr\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International
    See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
    See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).
    
    
    ----
    [<img width="80px" src="fidle/img/logo-paysage.svg"></img>](#top)

--- a/README.md
+++ b/README.md
@@ -32,14 +32,14 @@ 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 -->3.0.9<!-- VERSION_END -->
+Current Version : <!-- VERSION_BEGIN -->3.0.10<!-- VERSION_END -->


 ## Course materials

-| | | | |
+| Courses | Notebooks | Datasets | Videos |
 |:--:|:--:|:--:|:--:|
-| **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/datasets-fidle.tar)**<br>All the needed datasets<br>|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|
+| [<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>**Course slides**](https://fidle.cnrs.fr/supports)<br>The course in pdf format<br>| [<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>**Notebooks**](https://fidle.cnrs.fr/notebooks)<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>| [<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>**Datasets**](https://fidle.cnrs.fr/datasets-fidle.tar)<br>All the needed datasets<br>|[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>**Videos**](https://fidle.cnrs.fr/youtube)<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|

 Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.

@@ -47,7 +47,7 @@ Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)
 ## Jupyter notebooks

 <!-- TOC_BEGIN -->
-<!-- Automatically generated on : 03/03/24 20:38:37 -->
+<!-- Automatically generated on : 20/03/24 21:58:23 -->

 ### Linear and logistic regression
 - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)  
@@ -186,6 +186,8 @@ PyTorch est l'un des principaux framework utilisé dans le Deep Learning
 4 ways to use Tensorboard from the Jupyter environment
 - **[??](Misc/05-RNN.ipynb)** - [??](Misc/05-RNN.ipynb)  
 ??
+- **[??](Misc/06-Gradients.ipynb)** - [??](Misc/06-Gradients.ipynb)  
+??
 <!-- TOC_END -->

 ## Installation

--- a/fidle/about.yml
+++ b/fidle/about.yml
@@ -13,7 +13,7 @@
 #
 # This file describes the notebooks used by the Fidle training.

-version:                  3.0.9
+version:                  3.0.10
 content:                  notebooks
 name:                     Notebooks Fidle
 description:              All notebooks used by the Fidle training

--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
 campain:
  version: '1.0'
-  description: Automatically generated ci profile (03/03/24 20:38:37)
+  description: Automatically generated ci profile (20/03/24 21:58:23)
  directory: ./campains/default
  existing_notebook: 'remove    # remove|skip'
  report_template: 'fidle     # fidle|default'
@@ -321,4 +321,4 @@ TSB1:
  notebook: Misc/04-Using-Tensorboard.ipynb
  overrides: ??
 ??:
-  notebook: Misc/05-RNN.ipynb
+  notebook: Misc/06-Gradients.ipynb