A propos

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 :
Current Version : 1.2b1 DEV

Course materials

Course slides The course in pdf format (12 Mo)	Notebooks     Get a Zip or clone this repository      (10 Mo)	Datasets All the needed datasets (1.2 Go)

Have a look about How to get and install these notebooks and datasets.

Jupyter notebooks

LINR1	Linear regression with direct resolution Direct determination of linear regression
GRAD1	Linear regression with gradient descent An example of gradient descent in the simple case of a linear regression.
POLR1	Complexity Syndrome Illustration of the problem of complexity with the polynomial regression
LOGR1	Logistic regression, with sklearn Logistic Regression using Sklearn
PER57	Perceptron Model 1957 A simple perceptron, with the IRIS dataset.
MNIST1	Simple classification with DNN Example of classification with a fully connected neural network
GTSRB1	CNN with GTSRB dataset - Data analysis and preparation Episode 1 : Data analysis and creation of a usable dataset
GTSRB2	CNN with GTSRB dataset - First convolutions Episode 2 : First convolutions and first results
GTSRB3	CNN with GTSRB dataset - Monitoring Episode 3 : Monitoring and analysing training, managing checkpoints
GTSRB4	CNN with GTSRB dataset - Data augmentation Episode 4 : Improving the results with data augmentation
GTSRB5	CNN with GTSRB dataset - Full convolutions Episode 5 : A lot of models, a lot of datasets and a lot of results.
GTSRB6	Full convolutions as a batch Episode 6 : Run Full convolution notebook as a batch
GTSRB7	CNN with GTSRB dataset - Show reports Episode 7 : Displaying a jobs report
GTSRB10	OAR batch submission Bash script for OAR batch submission of GTSRB notebook
GTSRB11	SLURM batch script Bash script for SLURM batch submission of GTSRB notebooks
IMDB1	Text embedding with IMDB A very classical example of word embedding for text classification (sentiment analysis)
IMDB2	Text embedding with IMDB - Reloaded Example of reusing a previously saved model
IMDB3	Text embedding/LSTM model with IMDB Still the same problem, but with a network combining embedding and LSTM
SYNOP1	Time series with RNN - Preparation of data Episode 1 : Data analysis and creation of a usable dataset
SYNOP2	Time series with RNN - Try a prediction Episode 2 : Training session and first predictions
SYNOP3	Time series with RNN - 12h predictions Episode 3: Attempt to predict in the longer term
VAE1	Variational AutoEncoder (VAE) with MNIST Building a simple model with the MNIST dataset
VAE2	Variational AutoEncoder (VAE) with MNIST - Analysis Visualization and analysis of latent space
VAE3	About the CelebA dataset Presentation of the CelebA dataset and problems related to its size
VAE6	Preparation of the CelebA dataset Preparation of a clustered dataset, batchable
VAE7	Checking the clustered CelebA dataset Check the clustered dataset
VAE8	Variational AutoEncoder (VAE) with CelebA Building a VAE and train it, using a data generator
VAE9	Variational AutoEncoder (VAE) with CelebA - Analysis Exploring latent space of our trained models
VAE10	SLURM batch script Bash script for SLURM batch submission of VAE notebooks
ACTF1	Activation functions Some activation functions, with their derivatives.
NP1	A short introduction to Numpy Numpy is an essential tool for the Scientific Python.
TSB1	Tensorboard with/from Jupyter 4 ways to use Tensorboard from the Jupyter environment

Installation

A procedure for configuring and starting Jupyter is available in the Wiki.

Licence

[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.
See Disclaimer.

---