 ## 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 : - **[Configuration SSH](../-/wikis/howto-ssh)** - [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 ## 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](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/#).