Newer
Older
<a name="top"></a>
[<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#top)
<!-- --------------------------------------------------- -->
<!-- To correctly view this README under Jupyter Lab -->
<!-- Open the notebook: README.ipynb! -->
<!-- --------------------------------------------------- -->
This repository contains all the documents and links of the **Fidle Training** .
Fidle (for Formation Introduction au Deep Learning) is a 2-day training session
co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks.
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, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
**[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img>
Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**
[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle) notebooks and datasets
Some other useful informations are also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
<!-- DO NOT REMOVE THIS TAG !!! -->
[[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.
A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle)**.
[<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>](#top)