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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/Keras and Jupyter lab** technologies on the GPU
- Apprehend the **academic computing environments** Tier-2 (meso) and/or Tier-1 (national)
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)
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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
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/#).