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%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from IPython.display import display,Markdown from IPython.display import display,Markdown
display(Markdown(open('README.md', 'r').read())) display(Markdown(open('README.md', 'r').read()))
# #
# This README is visible under Jupiter LAb ! :-) # This README is visible under Jupiter LAb ! :-)
``` ```
%% Output %% Output
[<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#) [<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#)
<!-- --------------------------------------------------- --> <!-- --------------------------------------------------- -->
<!-- To correctly view this README under Jupyter Lab --> <!-- To correctly view this README under Jupyter Lab -->
<!-- Open the notebook: README.ipynb! --> <!-- Open the notebook: README.ipynb! -->
<!-- --------------------------------------------------- --> <!-- --------------------------------------------------- -->
## A propos ## A propos
This repository contains all the documents and links of the **Fidle Training** . This repository contains all the documents and links of the **Fidle Training** .
Fidle (for Formation Introduction au Deep Learning) is a 2-day training session Fidle (for Formation Introduction au Deep Learning) is a 2-day training session
co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks. co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks.
The objectives of this training,, are : The objectives of this training are :
- Understanding the **bases of Deep Learning** neural networks - Understanding the **bases of Deep Learning** neural networks
- Develop a **first experience** through simple and representative examples - Develop a **first experience** through simple and representative examples
- Understanding **Tensorflow/Keras** and **Jupyter lab** technologies - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
- Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
Current Version : 0.5 For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#)
Current Version : 0.5.1
## Course materials ## Course materials
**[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img> **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img>
Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/nx9axb3Yz2Gd7F5)** Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**
[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle) notebooks and datasets [How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle) notebooks and datasets
Some other useful informations are also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home) Some other useful informations are also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
## Jupyter notebooks ## Jupyter notebooks
<!-- DO NOT REMOVE THIS TAG !!! --> <!-- DO NOT REMOVE THIS TAG !!! -->
<!-- INDEX --> <!-- INDEX -->
<!-- INDEX_BEGIN --> <!-- INDEX_BEGIN -->
[[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) [[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Direct determination of linear regression &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Direct determination of linear regression
[[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) [[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.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;An example of gradient descent in the simple case of a linear regression.
[[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) [[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Illustration of the problem of complexity with the polynomial regression &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Illustration of the problem of complexity with the polynomial regression
[[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb) [[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.
[[PER57] - Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb) [[PER57] - Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A simple perceptron, with the IRIS dataset. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A simple perceptron, with the IRIS dataset.
[[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) [[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 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
[[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) [[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;More advanced example of DNN network code - BHPD dataset &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;More advanced example of DNN network code - BHPD dataset
[[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) [[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of classification with a fully connected neural network &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of classification with a fully connected neural network
[[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) [[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 &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) [[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 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : First convolutions and first results
[[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb) [[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3 : Monitoring and analysing training, managing checkpoints &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) [[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 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 4 : Improving the results with data augmentation
[[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb) [[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. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 5 : A lot of models, a lot of datasets and a lot of results.
[[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.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 &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) [[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 7 : Displaying the reports of the different jobs &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 7 : Displaying the reports of the different jobs
[[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb) [[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 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4 ways to use Tensorboard from the Jupyter environment
[[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb) [[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) &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) [[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of reusing a previously saved model &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) [[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 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Still the same problem, but with a network combining embedding and LSTM
[[SYNOP1] - Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb) [[SYNOP1] - Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1 : Data analysis and creation of a usable dataset &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1 : Data analysis and creation of a usable dataset
[[SYNOP2] - Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb) [[SYNOP2] - Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : Training session and first predictions &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : Training session and first predictions
[[SYNOP3] - Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb) [[SYNOP3] - Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3: Attempt to predict in the longer term &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3: Attempt to predict in the longer term
[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb) [[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1 : Model construction and Training &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1 : Model construction and Training
[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb) [[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : Exploring our latent space &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : Exploring our latent space
[[VAE3] - About the CelebA dataset](VAE/03-About-CelebA.ipynb) [[VAE3] - About the CelebA dataset](VAE/03-About-CelebA.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3 : About the CelebA dataset, a more fun dataset ! &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3 : About the CelebA dataset, a more fun dataset !
[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb) [[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 4 : Preparation of a clustered dataset, batchable &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 4 : Preparation of a clustered dataset, batchable
[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb) [[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 5 :\tChecking the clustered dataset &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 5 :\tChecking the clustered dataset
[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb) [[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.)
[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb) [[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.)
[[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb) [[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 8 : Exploring latent space of our trained models &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 8 : Exploring latent space of our trained models
[[BASH1] - OAR batch script](VAE/batch-oar.sh) [[BASH1] - OAR batch script](VAE/batch-oar.sh)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for OAR batch submission of a notebook &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for OAR batch submission of a notebook
[[BASH2] - SLURM batch script](VAE/batch-slurm.sh) [[BASH2] - SLURM batch script](VAE/batch-slurm.sh)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for SLURM batch submission of a notebook &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for SLURM batch submission of a notebook
[[ACTF1] - Activation functions](Misc/Activation-Functions.ipynb) [[ACTF1] - Activation functions](Misc/Activation-Functions.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Some activation functions, with their derivatives. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Some activation functions, with their derivatives.
[[NP1] - A short introduction to Numpy](Misc/Numpy.ipynb) [[NP1] - A short introduction to Numpy](Misc/Numpy.ipynb)
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Numpy is an essential tool for the Scientific Python. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Numpy is an essential tool for the Scientific Python.
<!-- INDEX_END --> <!-- INDEX_END -->
## Installation ## Installation
A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle)**. A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle)**.
## Licence ## Licence
[<img width="100px" src="fidle/img/00-fidle-CC BY-NC-SA.svg"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/) [<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) \[en\] 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 \[Fr\] 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 [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#). See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).
---- ----
[<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: %% Cell type:code id: tags:
``` python ``` python
``` ```
......
README.md
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...@@ -11,18 +11,20 @@ This repository contains all the documents and links of the **Fidle Training** . ...@@ -11,18 +11,20 @@ This repository contains all the documents and links of the **Fidle Training** .
Fidle (for Formation Introduction au Deep Learning) is a 2-day training session Fidle (for Formation Introduction au Deep Learning) is a 2-day training session
co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks. co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks.
The objectives of this training,, are : The objectives of this training are :
- Understanding the **bases of Deep Learning** neural networks - Understanding the **bases of Deep Learning** neural networks
- Develop a **first experience** through simple and representative examples - Develop a **first experience** through simple and representative examples
- Understanding **Tensorflow/Keras** and **Jupyter lab** technologies - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
- Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
Current Version : 0.5 For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#)
Current Version : 0.5.1
## Course materials ## Course materials
**[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img> **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img>
Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/nx9axb3Yz2Gd7F5)** Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/wxCztjYBbQ6zwd6)**
[How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle) notebooks and datasets [How to get and install](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/Install-Fidle) notebooks and datasets
Some other useful informations are also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home) Some other useful informations are also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
......
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