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"[<img width=\"600px\" src=\"fidle/img/00-Fidle-titre-01.svg\"></img>](#)\n",
"\n",
"<!-- --------------------------------------------------- -->\n",
"<!-- To correctly view this README under Jupyter Lab -->\n",
"<!-- Open the notebook: README.ipynb! -->\n",
"<!-- --------------------------------------------------- -->\n",
"\n",
"## A propos\n",
"\n",
"This repository contains all the documents and links of the **Fidle Training**. \n",
"\n",
"The objectives of this training, co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks, 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",
"Current Version : 0.4\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/f5T59gk3bxm4Dt9)** \n",
"\n",
"\n",
"\n",
"<!--  -->\n",
"Useful information is also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)\n",
"\n",
"\n",
"## Jupyter notebooks\n",
"\n",
"[](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Ftalks%2Fdeeplearning.git/master?urlpath=lab/tree/index.ipynb)\n",
"\n",
"\n",
"<!-- DO NOT REMOVE THIS TAG !!! -->\n",
"<!-- INDEX -->\n",
"<!-- INDEX_BEGIN -->\n",
"[[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) \n",
" Direct determination of linear regression \n",
"[[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) \n",
" An example of gradient descent in the simple case of a linear regression. \n",
"[[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) \n",
" Illustration of the problem of complexity with the polynomial regression \n",
"[[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb) \n",
" Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. \n",
"[[PER57] - Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb) \n",
" A simple perceptron, with the IRIS dataset. \n",
"[[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) \n",
" 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) \n",
" More advanced example of DNN network code - BHPD dataset \n",
"[[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) \n",
" 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) \n",
" Episode 1 : Data analysis and creation of a usable dataset \n",
"[[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb) \n",
" Episode 2 : First convolutions and first results \n",
"[[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb) \n",
" Episode 3 : Monitoring and analysing training, managing checkpoints \n",
"[[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb) \n",
" Episode 4 : Improving the results with data augmentation \n",
"[[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb) \n",
" 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-Full-convolutions-batch.ipynb) \n",
" Episode 6 : Run Full convolution notebook as a batch \n",
"[[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb) \n",
" Episode 7 : Displaying the reports of the different jobs \n",
"[[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb) \n",
" 4 ways to use Tensorboard from the Jupyter environment \n",
"[[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb) \n",
" A very classical example of word embedding for text classification (sentiment analysis) \n",
"[[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb) \n",
" Example of reusing a previously saved model \n",
"[[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb) \n",
" 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) \n",
" Episode 1 : Data analysis and creation of a usable dataset \n",
"[[SYNOP2] - Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb) \n",
" Episode 2 : Training session and first predictions \n",
"[[SYNOP3] - Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb) \n",
" Episode 3: Attempt to predict in the longer term \n",
"[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) \n",
" Episode 1 : Model construction and Training \n",
"[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) \n",
" Episode 2 : Exploring our latent space \n",
"[[VAE3] - About the CelebA dataset](VAE/03-About-CelebA.ipynb) \n",
" Episode 3 : About the CelebA dataset, a more fun dataset ! \n",
"[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb) \n",
" Episode 4 : Preparation of a clustered dataset, batchable \n",
"[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb) \n",
" Episode 5 :\\tChecking the clustered dataset \n",
"[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb) \n",
" Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.) \n",
"[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb) \n",
" Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.) \n",
"[[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb) \n",
" Episode 8 : Exploring latent space of our trained models \n",
"[[BASH1] - OAR batch script](VAE/batch-oar.sh) \n",
" Bash script for OAR batch submission of a notebook \n",
"[[BASH2] - SLURM batch script](VAE/batch-slurm.sh) \n",
" Bash script for SLURM batch submission of a notebook \n",
"[[ACTF1] - Activation functions](Misc/Activation-Functions.ipynb) \n",
" Some activation functions, with their derivatives. \n",
"[[NP1] - A short introduction to Numpy](Misc/Numpy.ipynb) \n",
" 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/howto-jupyter)**.\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>](#)\n"
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"display(Markdown(open('README.md', 'r').read()))\n",
"#\n",
"# This README is visible under Jupiter LAb ! :-)"
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