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 course slides

Useful information is also available in the wiki

Jupyter notebooks

1. Linear regression with direct resolution
         Direct determination of linear regression
2. Linear regression with gradient descent
         An example of gradient descent in the simple case of a linear regression.
3. Complexity Syndrome
         Illustration of the problem of complexity with the polynomial regression
4. Logistic regression, in pure Tensorflow
         Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.
5. Regression with a Dense Network (DNN)
         A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
6. Regression with a Dense Network (DNN) - Advanced code
         More advanced example of DNN network code - BHPD dataset
7. CNN with GTSRB dataset - Data analysis and preparation
         Episode 1: Data analysis and creation of a usable dataset
8. CNN with GTSRB dataset - First convolutions
         Episode 2 : First convolutions and first results
9. CNN with GTSRB dataset - Monitoring
         Episode 3: Monitoring and analysing training, managing checkpoints
10. CNN with GTSRB dataset - Data augmentation
          Episode 4: Improving the results with data augmentation
11. CNN with GTSRB dataset - Full convolutions
          Episode 5: A lot of models, a lot of datasets and a lot of results.
12. CNN with GTSRB dataset - Full convolutions as a batch
          Episode 6 : Run Full convolution notebook as a batch
13. Tensorboard with/from Jupyter
          4 ways to use Tensorboard from the Jupyter environment
14. Text embedding with IMDB
          A very classical example of word embedding for text classification (sentiment analysis)
15. Text embedding with IMDB - Reloaded
          Example of reusing a previously saved model
16. Text embedding/LSTM model with IMDB
          Still the same problem, but with a network combining embedding and LSTM

Installation

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

• Python >3.5
• Numpy
• Tensorflow 2.0
• Scikit-image
• Scikit-learn
• Matplotlib
• Seaborn
• pyplot

For this, you can use the Anaconda distribution :

1. Installing Anaconda :
   https://www.anaconda.com/distribution

2. Installing the Fidle conda environment :
   # conda env create -f environment.yml

To manage conda environment see there
To start Jupyter Lab on your machine or on a GRICAD cluster, see 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.