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 (Deep Learning)
• Develop a first experience through simple and representative examples
• Understand the different types of networks, their architectures and their use cases.
• Understanding Tensorflow/Keras and Jupyter lab technologies on the GPU
• Apprehend the academic computing environments Tier-2 (meso) and/or Tier-1 (national)

Support and notebooks

Get the support of the presentations
Note that useful information is also available in the wiki

All examples and practical work are available as Jupyter notebooks :

1. Regression with a Dense Network (DNN)
         A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
2. Regression with a Dense Network (DNN) - Advanced code
         More advanced example of DNN network code - BHPD dataset
3. CNN with GTSRB dataset - Data analysis and preparation
         Episode 1: Data analysis and creation of a usable dataset

Installation

To run this examples, you need an environment with the following packages :

• Python >3.5
• numpy
• Tensorflow 2.0
• scikit-image
• scikit-learn
• Matplotlib
• seaborn
• pyplot

You can install such a predefined environment :

conda env create -f environment.yml

To manage conda environment see there

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.