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## 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/Kera**s and Jupyter lab** technologies on the GPU
- Apprehend the **academic computing environments** Tier-2 (meso) and/or Tier-1 (national)
## Available at this depot:
You will find here :
- the support of the presentations
- all the practical work, in the form of Jupyter notebooks
- sheets and practical information :
- [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)<br>
A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
- [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)<br>
More advanced example of DNN network code - BHPD dataset
To run this examples, you need an environment with the following packages :
- 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](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)
## 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](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).