{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"data": {
"text/markdown": [
"<a name=\"top\"></a>\n",
"\n",
"[<img width=\"600px\" src=\"fidle/img/00-Fidle-titre-01.svg\"></img>](#top)\n",
"\n",
"<!-- --------------------------------------------------- -->\n",
"<!-- To correctly view this README under Jupyter Lab -->\n",
"<!-- Open the notebook: README.ipynb! -->\n",
"<!-- --------------------------------------------------- -->\n",
"\n",
"\n",
"## A propos\n",
"\n",
"This repository contains all the documents and links of the **Fidle Training** . \n",
"Fidle (for Formation Introduction au Deep Learning) is a 2-day training session \n",
"co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks. \n",
"\n",
"The objectives of this training are :\n",
" - Understanding the **bases of Deep Learning** neural networks\n",
" - Develop a **first experience** through simple and representative examples\n",
" - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies\n",
" - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU\n",
"\n",
"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",
"Current Version : <!-- VERSION_BEGIN -->\n",
"0.6.0 DEV\n",
"<!-- VERSION_END -->\n",
"\n",
"\n",
"## Course materials\n",
"**[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img>\n",
"Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)** \n",
"\n",
"[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle) notebooks and datasets \n",
"Some other useful informations are also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)\n",
"\n",
"\n",
"## Jupyter notebooks\n",
"\n",
"<!-- INDEX_BEGIN -->\n",
"| | |\n",
"|--|--|\n",
"|LINR1| [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)<br>Direct determination of linear regression |\n",
"|GRAD1| [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)<br>An example of gradient descent in the simple case of a linear regression.|\n",
"|POLR1| [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)<br>Illustration of the problem of complexity with the polynomial regression|\n",
"|LOGR1| [Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)<br>Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. |\n",
"|PER57| [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)<br>A simple perceptron, with the IRIS dataset.|\n",
"|BHP1| [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)<br>A Simple regression with a Dense Neural Network (DNN) - BHPD dataset|\n",
"|BHP2| [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)<br>More advanced example of DNN network code - BHPD dataset|\n",
"|MNIST1| [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)<br>Example of classification with a fully connected neural network|\n",
"|GTS1| [CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|\n",
"|GTS2| [CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)<br>Episode 2 : First convolutions and first results|\n",
"|GTS3| [CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)<br>Episode 3 : Monitoring and analysing training, managing checkpoints|\n",
"|GTS4| [CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)<br>Episode 4 : Improving the results with data augmentation|\n",
"|GTS5| [CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)<br>Episode 5 : A lot of models, a lot of datasets and a lot of results.|\n",
"|GTS6| [CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)<br>Episode 6 : Run Full convolution notebook as a batch|\n",
"|GTS7| [CNN with GTSRB dataset - Show reports](GTSRB/07-Show-report.ipynb)<br>Episode 7 : Displaying the reports of the different jobs|\n",
"|TSB1| [Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)<br>4 ways to use Tensorboard from the Jupyter environment|\n",
"|IMDB1| [Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)<br>A very classical example of word embedding for text classification (sentiment analysis)|\n",
"|IMDB2| [Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)<br>Example of reusing a previously saved model|\n",
"|IMDB3| [Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)<br>Still the same problem, but with a network combining embedding and LSTM|\n",
"|SYNOP1| [Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb)<br>Episode 1 : Data analysis and creation of a usable dataset|\n",
"|SYNOP2| [Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb)<br>Episode 2 : Training session and first predictions|\n",
"|SYNOP3| [Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)<br>Episode 3: Attempt to predict in the longer term |\n",
"|VAE1| [Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.nbconvert.ipynb)<br>Episode 1 : Model construction and Training|\n",
"|VAE2| [Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)<br>Episode 2 : Exploring our latent space|\n",
"|VAE3| [About the CelebA dataset](VAE/03-About-CelebA.ipynb)<br>Episode 3 : About the CelebA dataset, a more fun dataset ;-)|\n",
"|VAE4| [Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-datasets.ipynb)<br>Episode 4 : Preparation of a clustered dataset, batchable|\n",
"|VAE5| [Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb)<br>Episode 5 :\tChecking the clustered dataset|\n",
"|VAE6| [Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.nbconvert.ipynb)<br>Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.)|\n",
"|VAE7| [Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.nbconvert.ipynb)<br>Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.)|\n",
"|VAE8| [Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb)<br>Episode 8 : Exploring latent space of our trained models|\n",
"|ACTF1| [Activation functions](Misc/Activation-Functions.ipynb)<br>Some activation functions, with their derivatives.|\n",
"|NP1| [A short introduction to Numpy](Misc/Numpy.ipynb)<br>Numpy is an essential tool for the Scientific Python.|\n",
"<!-- INDEX_END -->\n",
"\n",
"\n",
"## Installation\n",
"\n",
"A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle)**.\n",
"\n",
"## Licence\n",
"\n",
"[<img width=\"100px\" src=\"fidle/img/00-fidle-CC BY-NC-SA.svg\"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/) \n",
"\\[en\\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0) \n",
"\\[Fr\\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International \n",
"See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). \n",
"See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#). \n",
"\n",
"\n",
"----\n",
"[<img width=\"80px\" src=\"fidle/img/00-Fidle-logo-01.svg\"></img>](#top)\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display,Markdown\n",
"display(Markdown(open('README.md', 'r').read()))\n",
"#\n",
"# This README is visible under Jupiter LAb ! :-)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}