[<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#) <!-- --------------------------------------------------- --> <!-- To correctly view this README under Jupyter Lab --> <!-- Open the notebook: README.ipynb! --> <!-- --------------------------------------------------- --> ## A propos This repository contains all the documents and links of the **Fidle Training**. The objectives of this training, co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks, 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 Current Version : 0.4 ## Course materials **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img> Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/f5T59gk3bxm4Dt9)** <!--  --> Useful information is also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home) ## Jupyter notebooks [](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Ftalks%2Fdeeplearning.git/master?urlpath=lab/tree/index.ipynb) <!-- DO NOT REMOVE THIS TAG !!! --> <!-- INDEX --> <!-- INDEX_BEGIN --> [[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) Direct determination of linear regression [[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) An example of gradient descent in the simple case of a linear regression. [[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) Illustration of the problem of complexity with the polynomial regression [[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb) Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. [[PER57] - Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb) A simple perceptron, with the IRIS dataset. [[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) A Simple regression with a Dense Neural Network (DNN) - BHPD dataset [[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) More advanced example of DNN network code - BHPD dataset [[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) Example of classification with a fully connected neural network [[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) Episode 1 : Data analysis and creation of a usable dataset [[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb) Episode 2 : First convolutions and first results [[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb) Episode 3 : Monitoring and analysing training, managing checkpoints [[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb) Episode 4 : Improving the results with data augmentation [[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb) Episode 5 : A lot of models, a lot of datasets and a lot of results. [[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb) Episode 6 : Run Full convolution notebook as a batch [[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb) Episode 7 : Displaying the reports of the different jobs [[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb) 4 ways to use Tensorboard from the Jupyter environment [[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb) A very classical example of word embedding for text classification (sentiment analysis) [[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb) Example of reusing a previously saved model [[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb) Still the same problem, but with a network combining embedding and LSTM [[SYNOP1] - Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb) Episode 1 : Data analysis and creation of a usable dataset [[SYNOP2] - Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb) Episode 2 : Training session and first predictions [[SYNOP3] - Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb) Episode 3: Attempt to predict in the longer term [[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) Episode 1 : Model construction and Training [[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) Episode 2 : Exploring our latent space [[VAE3] - About the CelebA dataset](VAE/03-About-CelebA.ipynb) Episode 3 : About the CelebA dataset, a more fun dataset ! [[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb) Episode 4 : Preparation of a clustered dataset, batchable [[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb) Episode 5 :\tChecking the clustered dataset [[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb) Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.) [[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb) Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.) [[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb) Episode 8 : Exploring latent space of our trained models [[BASH1] - OAR batch script](VAE/batch-oar.sh) Bash script for OAR batch submission of a notebook [[BASH2] - SLURM batch script](VAE/batch-slurm.sh) Bash script for SLURM batch submission of a notebook [[ACTF1] - Activation functions](Misc/Activation-Functions.ipynb) Some activation functions, with their derivatives. [[NP1] - A short introduction to Numpy](Misc/Numpy.ipynb) Numpy is an essential tool for the Scientific Python. <!-- INDEX_END --> ## Installation A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/howto-jupyter)**. ## 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/00-Fidle-logo-01.svg"></img>](#)