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%% Cell type:code id:e6482f7c tags: %% Cell type:code id:916506c0 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 ;-)# Automatically generated on : 17/10/22 19:16:06 # This README is visible under Jupiter Lab ;-)# Automatically generated on : 19/12/22 10:48:06
``` ```
%% Output %% Output
<a name="top"></a> <a name="top"></a>
[<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#top) [<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#top)
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<!-- To correctly view this README under Jupyter Lab --> <!-- To correctly view this README under Jupyter Lab -->
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## About Fidle ## About Fidle
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 3-day training session co-organized Fidle (for Formation Introduction au Deep Learning) is a 3-day training session co-organized
by the 3IA MIAI institute, the CNRS, via the Mission for Transversal and Interdisciplinary by the 3IA MIAI institute, the CNRS, via the Mission for Transversal and Interdisciplinary
Initiatives (MITI) and the University of Grenoble Alpes (UGA). Initiatives (MITI) and the University of Grenoble Alpes (UGA).
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
For more information, see **https://fidle.cnrs.fr** : For more information, see **https://fidle.cnrs.fr** :
- **[Fidle site](https://fidle.cnrs.fr)** - **[Fidle site](https://fidle.cnrs.fr)**
- **[Presentation of the training](https://fidle.cnrs.fr/presentation)** - **[Presentation of the training](https://fidle.cnrs.fr/presentation)**
- **[Program 2022/2023](https://fidle.cnrs.fr/programme)** - **[Program 2022/2023](https://fidle.cnrs.fr/programme)**
- [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed ! - [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !
- [Find us on youtube](https://fidle.cnrs.fr/youtube) - [Find us on youtube](https://fidle.cnrs.fr/youtube)
- [Corrected notebooks](https://fidle.cnrs.fr/done) - [Corrected notebooks](https://fidle.cnrs.fr/done)
For more information, you can contact us at : For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top) [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
Current Version : <!-- VERSION_BEGIN -->2.2.2<!-- VERSION_END --> Current Version : <!-- VERSION_BEGIN -->2.2.3<!-- VERSION_END -->
## Course materials ## Course materials
| | | | | | | | | |
|:--:|:--:|:--:|:--:| |:--:|:--:|:--:|:--:|
| **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;<br>&nbsp;| | **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/datasets-fidle.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;<br>&nbsp;|
Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets. Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
## Jupyter notebooks ## Jupyter notebooks
<!-- TOC_BEGIN --> <!-- TOC_BEGIN -->
<!-- Automatically generated on : 17/10/22 19:16:06 --> <!-- Automatically generated on : 19/12/22 10:48:06 -->
### Linear and logistic regression ### Linear and logistic regression
- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
Low-level implementation, using numpy, of a direct resolution for a linear regression Low-level implementation, using numpy, of a direct resolution for a linear regression
- **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb) - **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
Low level implementation of a solution by gradient descent. Basic and stochastic approach. Low level implementation of a solution by gradient descent. Basic and stochastic approach.
- **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb) - **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
Illustration of the problem of complexity with the polynomial regression Illustration of the problem of complexity with the polynomial regression
- **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb) - **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb)
Simple example of logistic regression with a sklearn solution Simple example of logistic regression with a sklearn solution
### Perceptron Model 1957 ### Perceptron Model 1957
- **[PER57](IRIS/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb) - **[PER57](IRIS/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)
Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 ! Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !
### Basic regression using DN ### Basic regression using DN
- **[BHPD1](BHPD/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb) - **[BHPD1](BHPD/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD) Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
- **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb) - **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
A more advanced implementation of the precedent example A more advanced implementation of the precedent example
- **[WINE1](BHPD/03-DNN-Wine-Regression.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](BHPD/03-DNN-Wine-Regression.ipynb) - **[WINE1](BHPD/03-DNN-Wine-Regression.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](BHPD/03-DNN-Wine-Regression.ipynb)
Another example of regression, with a wine quality prediction! Another example of regression, with a wine quality prediction!
### Basic classification using a DN ### Basic classification using a DN
- **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb) - **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
An example of classification using a dense neural network for the famous MNIST dataset An example of classification using a dense neural network for the famous MNIST dataset
- **[MNIST2](MNIST/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST/02-CNN-MNIST.ipynb) - **[MNIST2](MNIST/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST/02-CNN-MNIST.ipynb)
An example of classification using a convolutional neural network for the famous MNIST dataset An example of classification using a convolutional neural network for the famous MNIST dataset
### Images classification with Convolutional Neural Networks (CNN ### Images classification with Convolutional Neural Networks (CNN
- **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb) - **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
- **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb) - **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb)
Episode 2 : First convolutions and first classification of our traffic signs Episode 2 : First convolutions and first classification of our traffic signs
- **[GTSRB3](GTSRB/03-Tracking-and-visualizing.ipynb)** - [Training monitoring](GTSRB/03-Tracking-and-visualizing.ipynb) - **[GTSRB3](GTSRB/03-Tracking-and-visualizing.ipynb)** - [Training monitoring](GTSRB/03-Tracking-and-visualizing.ipynb)
Episode 3 : Monitoring, analysis and check points during a training session Episode 3 : Monitoring, analysis and check points during a training session
- **[GTSRB4](GTSRB/04-Data-augmentation.ipynb)** - [Data augmentation ](GTSRB/04-Data-augmentation.ipynb) - **[GTSRB4](GTSRB/04-Data-augmentation.ipynb)** - [Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
Episode 4 : Adding data by data augmentation when we lack it, to improve our results Episode 4 : Adding data by data augmentation when we lack it, to improve our results
- **[GTSRB5](GTSRB/05-Full-convolutions.ipynb)** - [Full convolutions](GTSRB/05-Full-convolutions.ipynb) - **[GTSRB5](GTSRB/05-Full-convolutions.ipynb)** - [Full convolutions](GTSRB/05-Full-convolutions.ipynb)
Episode 5 : A lot of models, a lot of datasets and a lot of results. Episode 5 : A lot of models, a lot of datasets and a lot of results.
- **[GTSRB6](GTSRB/06-Notebook-as-a-batch.ipynb)** - [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb) - **[GTSRB6](GTSRB/06-Notebook-as-a-batch.ipynb)** - [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)
Episode 6 : To compute bigger, use your notebook in batch mode Episode 6 : To compute bigger, use your notebook in batch mode
- **[GTSRB7](GTSRB/07-Show-report.ipynb)** - [Batch reports](GTSRB/07-Show-report.ipynb) - **[GTSRB7](GTSRB/07-Show-report.ipynb)** - [Batch reports](GTSRB/07-Show-report.ipynb)
Episode 7 : Displaying our jobs report, and the winner is... Episode 7 : Displaying our jobs report, and the winner is...
- **[GTSRB10](GTSRB/batch_oar.sh)** - [OAR batch script submission](GTSRB/batch_oar.sh) - **[GTSRB10](GTSRB/batch_oar.sh)** - [OAR batch script submission](GTSRB/batch_oar.sh)
Bash script for an OAR batch submission of an ipython code Bash script for an OAR batch submission of an ipython code
- **[GTSRB11](GTSRB/batch_slurm.sh)** - [SLURM batch script](GTSRB/batch_slurm.sh) - **[GTSRB11](GTSRB/batch_slurm.sh)** - [SLURM batch script](GTSRB/batch_slurm.sh)
Bash script for a Slurm batch submission of an ipython code Bash script for a Slurm batch submission of an ipython code
### Sentiment analysis with word embeddin ### Sentiment analysis with word embeddin
- **[IMDB1](IMDB/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](IMDB/01-One-hot-encoding.ipynb) - **[IMDB1](IMDB/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](IMDB/01-One-hot-encoding.ipynb)
A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB) A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB)
- **[IMDB2](IMDB/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](IMDB/02-Keras-embedding.ipynb) - **[IMDB2](IMDB/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](IMDB/02-Keras-embedding.ipynb)
A very classical example of word embedding with a dataset from Internet Movie Database (IMDB) A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)
- **[IMDB3](IMDB/03-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/03-Prediction.ipynb) - **[IMDB3](IMDB/03-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/03-Prediction.ipynb)
Retrieving a saved model to perform a sentiment analysis (movie review) Retrieving a saved model to perform a sentiment analysis (movie review)
- **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb) - **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb)
Retrieving embedded vectors from our trained model Retrieving embedded vectors from our trained model
- **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb) - **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb)
Still the same problem, but with a network combining embedding and RNN Still the same problem, but with a network combining embedding and RNN
### Time series with Recurrent Neural Network (RNN ### Time series with Recurrent Neural Network (RNN
- **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb) - **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb)
Artificial dataset generation and prediction attempt via a recurrent network Artificial dataset generation and prediction attempt via a recurrent network
- **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb) - **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb)
Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP) Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP)
- **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb) - **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb)
Episode 2 : RNN training session for weather prediction attempt at 3h Episode 2 : RNN training session for weather prediction attempt at 3h
- **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb) - **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb)
Episode 3: Attempt to predict in a more longer term Episode 3: Attempt to predict in a more longer term
### Sentiment analysis with transformer ### Sentiment analysis with transformer
- **[TRANS1](Transformers/01-Distilbert.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/01-Distilbert.ipynb) - **[TRANS1](Transformers/01-Distilbert.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/01-Distilbert.ipynb)
Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version
- **[TRANS2](Transformers/02-distilbert_colab.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/02-distilbert_colab.ipynb) - **[TRANS2](Transformers/02-distilbert_colab.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/02-distilbert_colab.ipynb)
Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version
### Unsupervised learning with an autoencoder neural network (AE ### Unsupervised learning with an autoencoder neural network (AE
- **[AE1](AE/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE/01-Prepare-MNIST-dataset.ipynb) - **[AE1](AE/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE/01-Prepare-MNIST-dataset.ipynb)
Episode 1: Preparation of a noisy MNIST dataset Episode 1: Preparation of a noisy MNIST dataset
- **[AE2](AE/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/02-AE-with-MNIST.ipynb) - **[AE2](AE/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/02-AE-with-MNIST.ipynb)
Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset. Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.
- **[AE3](AE/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE/03-AE-with-MNIST-post.ipynb) - **[AE3](AE/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE/03-AE-with-MNIST-post.ipynb)
Episode 2 : Using the previously trained autoencoder to denoise data Episode 2 : Using the previously trained autoencoder to denoise data
- **[AE4](AE/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE/04-ExtAE-with-MNIST.ipynb) - **[AE4](AE/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE/04-ExtAE-with-MNIST.ipynb)
Episode 4 : Construction of a denoiser and classifier model Episode 4 : Construction of a denoiser and classifier model
- **[AE5](AE/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE/05-ExtAE-with-MNIST.ipynb) - **[AE5](AE/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE/05-ExtAE-with-MNIST.ipynb)
Episode 5 : Construction of an advanced denoiser and classifier model Episode 5 : Construction of an advanced denoiser and classifier model
### Generative network with Variational Autoencoder (VAE ### Generative network with Variational Autoencoder (VAE
- **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, using functional API (MNIST dataset)](VAE/01-VAE-with-MNIST.ipynb) - **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, using functional API (MNIST dataset)](VAE/01-VAE-with-MNIST.ipynb)
Construction and training of a VAE, using functional APPI, with a latent space of small dimension. Construction and training of a VAE, using functional APPI, with a latent space of small dimension.
- **[VAE2](VAE/02-VAE-with-MNIST.ipynb)** - [VAE, using a custom model class (MNIST dataset)](VAE/02-VAE-with-MNIST.ipynb) - **[VAE2](VAE/02-VAE-with-MNIST.ipynb)** - [VAE, using a custom model class (MNIST dataset)](VAE/02-VAE-with-MNIST.ipynb)
Construction and training of a VAE, using model subclass, with a latent space of small dimension. Construction and training of a VAE, using model subclass, with a latent space of small dimension.
- **[VAE3](VAE/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE/03-VAE-with-MNIST-post.ipynb) - **[VAE3](VAE/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE/03-VAE-with-MNIST-post.ipynb)
Visualization and analysis of the VAE's latent space of the dataset MNIST Visualization and analysis of the VAE's latent space of the dataset MNIST
- **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb) - **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb)
Episode 1 : Presentation of the CelebA dataset and problems related to its size Episode 1 : Presentation of the CelebA dataset and problems related to its size
- **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb) - **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb)
Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset
- **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb) - **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb)
Episode : 3 Clustered dataset verification and testing of our datagenerator Episode : 3 Clustered dataset verification and testing of our datagenerator
- **[VAE8](VAE/08-VAE-with-CelebA-128x128.ipynb)** - [Training session for our VAE with 128x128 images](VAE/08-VAE-with-CelebA-128x128.ipynb) - **[VAE8](VAE/08-VAE-with-CelebA-128x128.ipynb)** - [Training session for our VAE with 128x128 images](VAE/08-VAE-with-CelebA-128x128.ipynb)
Episode 4 : Training with our clustered datasets in notebook or batch mode Episode 4 : Training with our clustered datasets in notebook or batch mode
- **[VAE9](VAE/09-VAE-with-CelebA-192x160.ipynb)** - [Training session for our VAE with 192x160 images](VAE/09-VAE-with-CelebA-192x160.ipynb) - **[VAE9](VAE/09-VAE-with-CelebA-192x160.ipynb)** - [Training session for our VAE with 192x160 images](VAE/09-VAE-with-CelebA-192x160.ipynb)
Episode 4 : Training with our clustered datasets in notebook or batch mode Episode 4 : Training with our clustered datasets in notebook or batch mode
- **[VAE10](VAE/10-VAE-with-CelebA-post.ipynb)** - [Data generation from latent space](VAE/10-VAE-with-CelebA-post.ipynb) - **[VAE10](VAE/10-VAE-with-CelebA-post.ipynb)** - [Data generation from latent space](VAE/10-VAE-with-CelebA-post.ipynb)
Episode 5 : Exploring latent space to generate new data Episode 5 : Exploring latent space to generate new data
- **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh) - **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh)
Bash script for SLURM batch submission of VAE8 notebooks Bash script for SLURM batch submission of VAE8 notebooks
### Generative Adversarial Networks (GANs ### Generative Adversarial Networks (GANs
- **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb) - **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)
Episode 1 : Draw me a sheep, revisited with a DCGAN Episode 1 : Draw me a sheep, revisited with a DCGAN
- **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb) - **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)
Episode 2 : Draw me a sheep, revisited with a WGAN-GP Episode 2 : Draw me a sheep, revisited with a WGAN-GP
### Deep Reinforcement Learning (DRL ### Deep Reinforcement Learning (DRL
- **[DRL1](DRL/FIDLE_DQNfromScratch.ipynb)** - [Solving CartPole with DQN](DRL/FIDLE_DQNfromScratch.ipynb) - **[DRL1](DRL/FIDLE_DQNfromScratch.ipynb)** - [Solving CartPole with DQN](DRL/FIDLE_DQNfromScratch.ipynb)
Using a a Deep Q-Network to play CartPole - an inverted pendulum problem (PyTorch) Using a a Deep Q-Network to play CartPole - an inverted pendulum problem (PyTorch)
- **[DRL2](DRL/FIDLE_rl_baselines_zoo.ipynb)** - [RL Baselines3 Zoo: Training in Colab](DRL/FIDLE_rl_baselines_zoo.ipynb) - **[DRL2](DRL/FIDLE_rl_baselines_zoo.ipynb)** - [RL Baselines3 Zoo: Training in Colab](DRL/FIDLE_rl_baselines_zoo.ipynb)
Demo of Stable baseline3 with Colab Demo of Stable baseline3 with Colab
### Miscellaneous ### Miscellaneous
- **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb) - **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb)
Some activation functions, with their derivatives. Some activation functions, with their derivatives.
- **[NP1](Misc/Numpy.ipynb)** - [A short introduction to Numpy](Misc/Numpy.ipynb) - **[NP1](Misc/Numpy.ipynb)** - [A short introduction to Numpy](Misc/Numpy.ipynb)
Numpy is an essential tool for the Scientific Python. Numpy is an essential tool for the Scientific Python.
- **[SCRATCH1](Misc/Scratchbook.ipynb)** - [Scratchbook](Misc/Scratchbook.ipynb) - **[SCRATCH1](Misc/Scratchbook.ipynb)** - [Scratchbook](Misc/Scratchbook.ipynb)
A scratchbook for small examples A scratchbook for small examples
- **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb) - **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)
4 ways to use Tensorboard from the Jupyter environment 4 ways to use Tensorboard from the Jupyter environment
- **[PANDAS1](Misc/Using-pandas.ipynb)** - [Quelques exemples avec Pandas](Misc/Using-pandas.ipynb) - **[PANDAS1](Misc/Using-pandas.ipynb)** - [Quelques exemples avec Pandas](Misc/Using-pandas.ipynb)
pandas is another essential tool for the Scientific Python. pandas is another essential tool for the Scientific Python.
<!-- TOC_END --> <!-- TOC_END -->
## Installation ## Installation
Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets. Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
## 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>](#top) [<img width="80px" src="fidle/img/00-Fidle-logo-01.svg"></img>](#top)
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README.md
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...@@ -31,14 +31,14 @@ For more information, see **https://fidle.cnrs.fr** : ...@@ -31,14 +31,14 @@ For more information, see **https://fidle.cnrs.fr** :
For more information, you can contact us at : For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top) [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
Current Version : <!-- VERSION_BEGIN -->2.2.2<!-- VERSION_END --> Current Version : <!-- VERSION_BEGIN -->2.2.3<!-- VERSION_END -->
## Course materials ## Course materials
| | | | | | | | | |
|:--:|:--:|:--:|:--:| |:--:|:--:|:--:|:--:|
| **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;<br>&nbsp;| | **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/datasets-fidle.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;<br>&nbsp;|
Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets. Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
...@@ -46,7 +46,7 @@ Have a look about **[How to get and install](https://fidle.cnrs.fr/installation) ...@@ -46,7 +46,7 @@ Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)
## Jupyter notebooks ## Jupyter notebooks
<!-- TOC_BEGIN --> <!-- TOC_BEGIN -->
<!-- Automatically generated on : 17/10/22 19:16:06 --> <!-- Automatically generated on : 19/12/22 10:48:06 -->
### Linear and logistic regression ### Linear and logistic regression
- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
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docker/fidle_python_pip.dockerfile
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# #
# #
ARG PYTHON_VERSION=3.8 ARG PYTHON_VERSION=3.7
ARG docker_image_base=python:${PYTHON_VERSION}-slim ARG docker_image_base=python:${PYTHON_VERSION}-slim
FROM ${docker_image_base} FROM ${docker_image_base}
LABEL maintainer=soraya.arias@inria.fr LABEL maintainer=soraya.arias@inria.fr
# Ensure a sane environment # Ensure a sane environment
ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 DEBIAN_FRONTEND=noninteractive ENV TZ=Europe/Paris LANG=C.UTF-8 LC_ALL=C.UTF-8 DEBIAN_FRONTEND=noninteractive
RUN apt update --fix-missing && \ RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone && \
apt install -y --no-install-recommends apt-utils \ apt update --fix-missing && \
python3-venv \ apt install -y --no-install-recommends apt-utils &&\
python3-pip && \ apt install -y wget curl git \
python3-venv python3-pip && \
apt -y dist-upgrade && \ apt -y dist-upgrade && \
curl -fsSL https://deb.nodesource.com/setup_lts.x | bash - && \
apt install -y nodejs && \
apt clean && \ apt clean && \
rm -fr /var/lib/apt/lists/* rm -fr /var/lib/apt/lists/*
...@@ -39,6 +42,8 @@ COPY notebook.json /root/.jupyter/nbconfig/notebook.json ...@@ -39,6 +42,8 @@ COPY notebook.json /root/.jupyter/nbconfig/notebook.json
# Jupyter notebook uses 8888 # Jupyter notebook uses 8888
EXPOSE 8888 EXPOSE 8888
# Tensor board uses 6006
EXPOSE 6006
VOLUME /notebooks VOLUME /notebooks
WORKDIR /notebooks WORKDIR /notebooks
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docker/jupyter_lab_config.py
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...@@ -902,6 +902,7 @@ c.ServerApp.quit_button = True ...@@ -902,6 +902,7 @@ c.ServerApp.quit_button = True
# Default: '' # Default: ''
c.ServerApp.root_dir = '/notebooks/fidle-master' c.ServerApp.root_dir = '/notebooks/fidle-master'
## The session manager class to use. ## The session manager class to use.
# Default: 'jupyter_server.services.sessions.sessionmanager.SessionManager' # Default: 'jupyter_server.services.sessions.sessionmanager.SessionManager'
# c.ServerApp.session_manager_class = 'jupyter_server.services.sessions.sessionmanager.SessionManager' # c.ServerApp.session_manager_class = 'jupyter_server.services.sessions.sessionmanager.SessionManager'
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docker/requirements.txt 0 → 100644
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tensorflow_cpu
Scikit-image
Scikit-learn
Matplotlib
Pandas
Pandoc
pyyaml
torch
torchvision
torchaudio
Jupyterlab
fidle/about.yml
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...@@ -13,7 +13,7 @@ ...@@ -13,7 +13,7 @@
# #
# This file describes the notebooks used by the Fidle training. # This file describes the notebooks used by the Fidle training.
version: 2.2.2 version: 2.2.3
content: notebooks content: notebooks
name: Notebooks Fidle name: Notebooks Fidle
description: All notebooks used by the Fidle training description: All notebooks used by the Fidle training
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fidle/ci/default.yml
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campain: campain:
version: '1.0' version: '1.0'
description: Automatically generated ci profile (17/10/22 19:16:06) description: Automatically generated ci profile (19/12/22 10:48:06)
directory: ./campains/default directory: ./campains/default
existing_notebook: 'remove # remove|skip' existing_notebook: 'remove # remove|skip'
report_template: 'fidle # fidle|default' report_template: 'fidle # fidle|default'
......
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