 ## 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) ## Course materials Get the **[support of the presentations](Bientot)** Useful information is also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home) **Jupyter notebooks :** <!-- DO NOT REMOVE THIS TAG !!! --> <!-- INDEX --> <!-- INDEX_BEGIN --> 1. [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)<br> Direct determination of linear regression 1. [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)<br> An example of gradient descent in the simple case of a linear regression. 1. [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)<br> Illustration of the problem of complexity with the polynomial regression 1. [Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)<br> Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. 1. [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)<br> A Simple regression with a Dense Neural Network (DNN) - BHPD dataset 1. [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)<br> More advanced example of DNN network code - BHPD dataset 1. [CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)<br> Episode 1: Data analysis and creation of a usable dataset 1. [CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)<br> Episode 2 : First convolutions and first results 1. [CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)<br> Episode 3: Monitoring and analysing training, managing checkpoints 1. [CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)<br> Episode 4: Improving the results with data augmentation 1. [CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)<br> Episode 5: A lot of models, a lot of datasets and a lot of results. 1. [CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)<br> Episode 6 : Run Full convolution notebook as a batch 1. [Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)<br> 4 ways to use Tensorboard from the Jupyter environment 1. [Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)<br> A very classical example of word embedding for text classification (sentiment analysis) 1. [Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)<br> Example of reusing a previously saved model 1. [Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)<br> Still the same problem, but with a network combining embedding and LSTM <!-- INDEX_END --> ## 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/#).