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%% Cell type:markdown id: tags: %% Cell type:code id: tags:
<img width="800px" src="fidle/img/00-Fidle-header-01.svg"></img> ``` python
from IPython.display import display,Markdown
# Available notebooks display(Markdown(open('README.md', 'r').read()))
```
%% Cell type:markdown id: tags:
%% Output
<!-- INDEX_BEGIN -->
[[NP1] - A short introduction to Numpy](Prerequisites/Numpy.ipynb) [<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Numpy is an essential tool for the Scientific Python.
[[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) ## A propos
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Direct determination of linear regression
[[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) This repository contains all the documents and links of the **Fidle Training**.
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;An example of gradient descent in the simple case of a linear regression.
[[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) The objectives of this training, co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks, are :
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Illustration of the problem of complexity with the polynomial regression - Understanding the **bases of deep learning** neural networks (Deep Learning)
[[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb) - Develop a **first experience** through simple and representative examples
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. - Understand the different types of networks, their **architectures** and their **use cases**.
[[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) - Understanding **Tensorflow/Keras and Jupyter lab** technologies on the GPU
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of classification with a fully connected neural network - Apprehend the **academic computing environments** Tier-2 (meso) and/or Tier-1 (national)
[[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A Simple regression with a Dense Neural Network (DNN) - BHPD dataset ## Course materials
[[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;More advanced example of DNN network code - BHPD dataset Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/z7XZA36xKkMcaTS)**
[[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1: Data analysis and creation of a usable dataset
[[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : First convolutions and first results <!-- ![pdf](fidle/img/00-Fidle-pdf.png) -->
[[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb) Useful information is also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3: Monitoring and analysing training, managing checkpoints
[[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 4: Improving the results with data augmentation ## Jupyter notebooks
[[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 5: A lot of models, a lot of datasets and a lot of results. [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Ftalks%2Fdeeplearning.git/master?urlpath=lab/tree/index.ipynb)
[[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 6 : Run Full convolution notebook as a batch
[[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb) <!-- DO NOT REMOVE THIS TAG !!! -->
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Displaying the reports of the different jobs <!-- INDEX -->
[[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb) <!-- INDEX_BEGIN -->
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4 ways to use Tensorboard from the Jupyter environment [[NP1] - A short introduction to Numpy](Prerequisites/Numpy.ipynb)
[[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Numpy is an essential tool for the Scientific Python.
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A very classical example of word embedding for text classification (sentiment analysis) [[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
[[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Direct determination of linear regression
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of reusing a previously saved model [[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
[[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;An example of gradient descent in the simple case of a linear regression.
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Still the same problem, but with a network combining embedding and LSTM [[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Illustration of the problem of complexity with the polynomial regression
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;First generative network experience with the MNIST dataset [[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)
[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Use of the previously trained model, analysis of the results [[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
[[VAE3] - About the CelebA dataset](VAE/03-Prepare-CelebA.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of classification with a fully connected neural network
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;New VAE experience, but with a larger and more fun dataset [[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Preparation of a clustered dataset, batchable [[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;More advanced example of DNN network code - BHPD dataset
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Verification of prepared data from CelebA dataset [[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1: Data analysis and creation of a usable dataset
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;VAE with a more fun and realistic dataset - small resolution and batchable [[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)
[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : First convolutions and first results
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;VAE with a more fun and realistic dataset - medium resolution and batchable [[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)
[[VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/12-VAE-withCelebA-post.ipynb) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3: Monitoring and analysing training, managing checkpoints
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Use of the previously trained model with CelebA, analysis of the results [[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
[[BASH1] - OAR batch script](VAE/batch-oar.sh) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 4: Improving the results with data augmentation
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for OAR batch submission of a notebook [[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)
[[BASH2] - SLURM batch script](VAE/batch-slurm.sh) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 5: A lot of models, a lot of datasets and a lot of results.
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for SLURM batch submission of a notebook [[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)
<!-- INDEX_END --> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 6 : Run Full convolution notebook as a batch
[[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb)
%% Cell type:markdown id: tags: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Displaying the reports of the different jobs
[[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)
--- &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4 ways to use Tensorboard from the Jupyter environment
<img width="80px" src="fidle/img/00-Fidle-logo-01.svg"></img> [[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A very classical example of word embedding for text classification (sentiment analysis)
[[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of reusing a previously saved model
[[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Still the same problem, but with a network combining embedding and LSTM
[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;First generative network experience with the MNIST dataset
[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Use of the previously trained model, analysis of the results
[[VAE3] - About the CelebA dataset](VAE/03-Prepare-CelebA.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;New VAE experience, but with a larger and more fun dataset
[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Preparation of a clustered dataset, batchable
[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Verification of prepared data from CelebA dataset
[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;VAE with a more fun and realistic dataset - small resolution and batchable
[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;VAE with a more fun and realistic dataset - medium resolution and batchable
[[VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/12-VAE-withCelebA-post.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Use of the previously trained model with CelebA, analysis of the results
[[BASH1] - OAR batch script](VAE/batch-oar.sh)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for OAR batch submission of a notebook
[[BASH2] - SLURM batch script](VAE/batch-slurm.sh)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for SLURM batch submission of a notebook
<!-- INDEX_END -->
## Installation
A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/howto-jupyter)**.
## Licence
[<img width="100px" src="fidle/img/00-fidle-CC BY-NC-SA.svg"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/)
\[en\] 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/#).
----
[<img width="80px" src="fidle/img/00-Fidle-logo-01.svg"></img>](#)
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%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
<img width="800px" src="../fidle/img/00-Fidle-header-01.svg"></img> <img width="800px" src="../fidle/img/00-Fidle-header-01.svg"></img>
# <!-- TITLE --> Titre_du_notebook # <!-- TITLE --> Titre_du_notebook
<!-- DESC --> Description_du_notebook_et_de_sa_thématique <!-- DESC --> Description_du_notebook_et_de_sa_thématique
<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) --> <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
## Objectives : ## Objectives :
- Objectif - Objectif
- Objectif_pédagogique - Objectif_pédagogique
A_propos_du_dataset A_propos_du_dataset
## What we're going to do : ## What we're going to do :
- Ceci - Ceci
- Cela - Cela
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
--- ---
<img width="80px" src="../fidle/img/00-Fidle-logo-01.svg"></img> <img width="80px" src="../fidle/img/00-Fidle-logo-01.svg"></img>
%% Cell type:code id: tags:
``` python
```
......
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