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"<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",
"## Session Fidle à distance (NEW !)\n",
"\n",
"Faute de pouvoir organiser des sessions en présentiel,\\\n",
"nous vous proposons une **session à distance** :-)\n",
"\n",
"**- Prochain rendez-vous -**\n",
"\n",
"|[<img width=\"100px\" src=\"fidle/img/00-Fidle-a-distance-01.svg\"></img>](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)<br>**Jeudi 4 févier, 14h :**<br>Episode 1 : **Introduction du cycle, Historique et concepts fondamentaux**|\n",
"|:---|\n",
"|Fonction de perte - Descente de gradient - Optimisation - Hyperparamètres<br>Préparation des données - Apprentissage - Validation - Sous et sur apprentissage<br>Fonctions d’activation - softmax<br>Travaux pratiques : Régression et Classification avec des DNN|\n",
"|Durée : 3h - Paramètres de diffusion précisés 2 jours avant|\n",
"\n",
"A propos de **[Fidle à distance](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/En%20bref)**\\\n",
"Voir le [programme](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Fidle%20%C3%A0%20distance/Pr%C3%A9sentation#programme-)\n",
"\n",
"\n",
"## About Fidle\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 Resinfo/SARI and DevLOG CNRS 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",
"2.0.1\n",
"<!-- VERSION_END -->\n",
"\n",
"\n",
"## Course materials\n",
"\n",
"| | | |\n",
"|:--:|:--:|:--:|\n",
"| **[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img><br>Course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>The course in pdf format<br>(12 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Notebooks.svg\"></img><br>Notebooks](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/archive/master/fidle-master.zip)**<br> Get a Zip or clone this repository <br>(10 Mo)| **[<img width=\"50px\" src=\"fidle/img/00-Datasets-tar.svg\"></img><br>Datasets](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**<br>All the needed datasets<br>(1.2 Go)|\n",
"\n",
"Have a look about **[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle)** these notebooks and datasets.\n",
"\n",
"\n",
"## Jupyter notebooks\n",
"\n",
"<!-- INDEX_BEGIN -->\n",
"\n",
"### Linear and logistic regression\n",
"- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) \n",
"Low-level implementation, using numpy, of a direct resolution for a linear regression\n",
"- **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) \n",
"Low level implementation of a solution by gradient descent. Basic and stochastic approach.\n",
"- **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) \n",
"Illustration of the problem of complexity with the polynomial regression\n",
"- **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb) \n",
"Simple example of logistic regression with a sklearn solution\n",
"\n",
"### Perceptron Model 1957\n",
"- **[PER57](IRIS/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb) \n",
"Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !\n",
"\n",
"### Basic regression using DNN\n",
"- **[BHPD1](BHPD/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) \n",
"Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)\n",
"- **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) \n",
"A more advanced implementation of the precedent example\n",
"\n",
"### Basic classification using a DNN\n",
"- **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) \n",
"An example of classification using a dense neural network for the famous MNIST dataset\n",
"\n",
"### Images classification with Convolutional Neural Networks (CNN)\n",
"- **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) \n",
"Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset\n",
"- **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb) \n",
"Episode 2 : First convolutions and first classification of our traffic signs\n",
"- **[GTSRB3](GTSRB/03-Tracking-and-visualizing.ipynb)** - [Training monitoring](GTSRB/03-Tracking-and-visualizing.ipynb) \n",
"Episode 3 : Monitoring, analysis and check points during a training session\n",
"- **[GTSRB4](GTSRB/04-Data-augmentation.ipynb)** - [Data augmentation ](GTSRB/04-Data-augmentation.ipynb) \n",
"Episode 4 : Adding data by data augmentation when we lack it, to improve our results\n",
"- **[GTSRB5](GTSRB/05-Full-convolutions.ipynb)** - [Full convolutions](GTSRB/05-Full-convolutions.ipynb) \n",
"Episode 5 : A lot of models, a lot of datasets and a lot of results.\n",
"- **[GTSRB6](GTSRB/06-Notebook-as-a-batch.ipynb)** - [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb) \n",
"Episode 6 : To compute bigger, use your notebook in batch mode\n",
"- **[GTSRB7](GTSRB/07-Show-report.ipynb)** - [Batch reports](GTSRB/07-Show-report.ipynb) \n",
"Episode 7 : Displaying our jobs report, and the winner is...\n",
"- **[GTSRB10](GTSRB/batch_oar.sh)** - [OAR batch script submission](GTSRB/batch_oar.sh) \n",
"Bash script for an OAR batch submission of an ipython code\n",
"- **[GTSRB11](GTSRB/batch_slurm.sh)** - [SLURM batch script](GTSRB/batch_slurm.sh) \n",
"Bash script for a Slurm batch submission of an ipython code\n",
"\n",
"### Sentiment analysis with word embedding\n",
"- **[IMDB1](IMDB/01-Embedding-Keras.ipynb)** - [Sentiment alalysis with text embedding](IMDB/01-Embedding-Keras.ipynb) \n",
"A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)\n",
"- **[IMDB2](IMDB/02-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/02-Prediction.ipynb) \n",
"Retrieving a saved model to perform a sentiment analysis (movie review)\n",
"- **[IMDB3](IMDB/03-LSTM-Keras.ipynb)** - [Sentiment analysis with a LSTM network](IMDB/03-LSTM-Keras.ipynb) \n",
"Still the same problem, but with a network combining embedding and LSTM\n",
"\n",
"### Time series with Recurrent Neural Network (RNN)\n",
"- **[SYNOP1](SYNOP/01-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/01-Preparation-of-data.ipynb) \n",
"Episode 1 : Data analysis and preparation of a meteorological dataset (SYNOP)\n",
"- **[SYNOP2](SYNOP/02-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/02-First-predictions.ipynb) \n",
"Episode 2 : Learning session and weather prediction attempt at 3h\n",
"- **[SYNOP3](SYNOP/03-12h-predictions.ipynb)** - [12h predictions](SYNOP/03-12h-predictions.ipynb) \n",
"Episode 3: Attempt to predict in a more longer term \n",
"\n",
"### Unsupervised learning with an autoencoder neural network (AE)\n",
"- **[AE1](AE/01-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/01-AE-with-MNIST.ipynb) \n",
"Episode 1 : After construction, the model is trained with noisy data from the MNIST dataset.\n",
"- **[AE2](AE/02-AE-with-MNIST-post.ipynb)** - [Exploring our denoiser model](AE/02-AE-with-MNIST-post.ipynb) \n",
"Episode 2 : Using the previously trained autoencoder to denoise data\n",
"\n",
"### Generative network with Variational Autoencoder (VAE)\n",
"- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, with a small dataset (MNIST)](VAE/01-VAE-with-MNIST.ipynb) \n",
"Construction and training of a VAE with a latent space of small dimension.\n",
"- **[VAE2](VAE/02-VAE-with-MNIST-post.ipynb)** - [Analysis of the associated latent space](VAE/02-VAE-with-MNIST-post.ipynb) \n",
"Visualization and analysis of the VAE's latent space\n",
"- **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb) \n",
"Episode 1 : Presentation of the CelebA dataset and problems related to its size\n",
"- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb) \n",
"Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset\n",
"- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb) \n",
"Episode : 3 Clustered dataset verification and testing of our datagenerator\n",
"- **[VAE8](VAE/08-VAE-with-CelebA.ipynb)** - [Training session for our VAE](VAE/08-VAE-with-CelebA.ipynb) \n",
"Episode 4 : Training with our clustered datasets in notebook or batch mode\n",
"- **[VAE9](VAE/09-VAE-withCelebA-post.ipynb)** - [Data generation from latent space](VAE/09-VAE-withCelebA-post.ipynb) \n",
"Episode 5 : Exploring latent space to generate new data\n",
"- **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh) \n",
"Bash script for SLURM batch submission of VAE8 notebooks \n",
"\n",
"### Miscellaneous\n",
"- **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb) \n",
"Some activation functions, with their derivatives.\n",
"- **[NP1](Misc/Numpy.ipynb)** - [A short introduction to Numpy](Misc/Numpy.ipynb) \n",
"Numpy is an essential tool for the Scientific Python.\n",
"- **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb) \n",
"4 ways to use Tensorboard from the Jupyter environment\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"
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"from IPython.display import display,Markdown\n",
"display(Markdown(open('README.md', 'r').read()))\n",
"#\n",
"# This README is visible under Jupiter LAb ! :-)"
]
}
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