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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
by the 3IA MIAI institute, the CNRS, via the Mission for Transversal and Interdisciplinary
Initiatives (MITI) and the University of Grenoble Alpes (UGA).
The objectives of this training are :
- Understanding the **bases of Deep Learning** neural networks
- Develop a **first experience** through simple and representative examples
- Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
- Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
- **[Presentation of the training](https://fidle.cnrs.fr/presentation)**
- **[Detailed program](https://fidle.cnrs.fr/programme)**
- **[Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !**
- **[Corrected notebooks](https://fidle.cnrs.fr/done)**
- **[Follow us on our channel :](https://fidle.cnrs.fr/youtube)**\
[<img width="120px" style="vertical-align:middle" src="fidle/img/logo-YouTube.png"></img>](https://fidle.cnrs.fr/youtube)
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Current Version : <!-- VERSION_BEGIN -->3.0.15<!-- VERSION_END -->
| [<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>| [<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>**Notebooks**](https://fidle.cnrs.fr/notebooks)<br> Get a Zip or clone this repository <br>| [<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>|[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>**Videos**](https://fidle.cnrs.fr/youtube)<br> Our Youtube channel |
Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
<!-- Automatically generated on : 06/01/25 16:42:30 -->
### Linear and logistic regression
- **[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
- **[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.
- **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
Illustration of the problem of complexity with the polynomial regression
- **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb)
Simple example of logistic regression with a sklearn solution
### Perceptron Model 1957
- **[PER57](Perceptron/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](Perceptron/01-Simple-Perceptron.ipynb)
Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !
### BHPD regression (DNN), using Keras3/PyTorch
- **[K3BHPD1](BHPD.Keras3/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD.Keras3/01-DNN-Regression.ipynb)
Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
- **[K3BHPD2](BHPD.Keras3/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD.Keras3/02-DNN-Regression-Premium.ipynb)
A more advanced implementation of the precedent example, using Keras3
### BHPD regression (DNN), using PyTorch
- **[PBHPD1](BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb)** - [Regression with a Dense Network (DNN)](BHPD.PyTorch/01-DNN-Regression_PyTorch.ipynb)
A Simple regression with a Dense Neural Network (DNN) using Pytorch - BHPD dataset
### Wine Quality prediction (DNN), using Keras3/PyTorch
- **[K3WINE1](Wine.Keras3/01-DNN-Wine-Regression.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](Wine.Keras3/01-DNN-Wine-Regression.ipynb)
Another example of regression, with a wine quality prediction, using Keras 3 and PyTorch
### Wine Quality prediction (DNN), using PyTorch/Lightning
- **[LWINE1](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](Wine.Lightning/01-DNN-Wine-Regression-lightning.ipynb)
Another example of regression, with a wine quality prediction, using PyTorch Lightning
### MNIST classification (DNN,CNN), using Keras3/PyTorch
- **[K3MNIST1](MNIST.Keras3/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST.Keras3/01-DNN-MNIST.ipynb)
An example of classification using a dense neural network for the famous MNIST dataset
- **[K3MNIST2](MNIST.Keras3/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST.Keras3/02-CNN-MNIST.ipynb)
An example of classification using a convolutional neural network for the famous MNIST dataset
### MNIST classification (DNN,CNN), using PyTorch
- **[PMNIST1](MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb)** - [Simple classification with DNN](MNIST.PyTorch/01-DNN-MNIST_PyTorch.ipynb)
Example of classification with a fully connected neural network, using Pytorch
### MNIST classification (DNN,CNN), using PyTorch/Lightning
- **[LMNIST1](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)** - [Simple classification with DNN](MNIST.Lightning/01-DNN-MNIST_Lightning.ipynb)
An example of classification using a dense neural network for the famous MNIST dataset, using PyTorch Lightning
- **[LMNIST2](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)** - [Simple classification with CNN](MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb)
An example of classification using a convolutional neural network for the famous MNIST dataset, using PyTorch Lightning
### Images classification GTSRB with Convolutional Neural Networks (CNN), using Keras3/PyTorch
- **[K3GTSRB1](GTSRB.Keras3/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB.Keras3/01-Preparation-of-data.ipynb)
Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
- **[K3GTSRB2](GTSRB.Keras3/02-First-convolutions.ipynb)** - [First convolutions](GTSRB.Keras3/02-First-convolutions.ipynb)
Episode 2 : First convolutions and first classification of our traffic signs, using Keras3
- **[K3GTSRB3](GTSRB.Keras3/03-Better-convolutions.ipynb)** - [Training monitoring](GTSRB.Keras3/03-Better-convolutions.ipynb)
Episode 3 : Monitoring, analysis and check points during a training session, using Keras3
- **[K3GTSRB10](GTSRB.Keras3/batch_oar.sh)** - [OAR batch script submission](GTSRB.Keras3/batch_oar.sh)
Bash script for an OAR batch submission of an ipython code
- **[K3GTSRB11](GTSRB.Keras3/batch_slurm.sh)** - [SLURM batch script](GTSRB.Keras3/batch_slurm.sh)
Bash script for a Slurm batch submission of an ipython code
### Sentiment analysis with word embedding, using Keras3/PyTorch
- **[K3IMDB1](Embedding.Keras3/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](Embedding.Keras3/01-One-hot-encoding.ipynb)
A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB), using Keras 3 on PyTorch
- **[K3IMDB2](Embedding.Keras3/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](Embedding.Keras3/02-Keras-embedding.ipynb)
A very classical example of word embedding with a dataset from Internet Movie Database (IMDB), using Keras 3 on PyTorch
- **[K3IMDB3](Embedding.Keras3/03-Prediction.ipynb)** - [Reload and reuse a saved model](Embedding.Keras3/03-Prediction.ipynb)
Retrieving a saved model to perform a sentiment analysis (movie review), using Keras 3 and PyTorch
- **[K3IMDB4](Embedding.Keras3/04-Show-vectors.ipynb)** - [Reload embedded vectors](Embedding.Keras3/04-Show-vectors.ipynb)
Retrieving embedded vectors from our trained model, using Keras 3 and PyTorch
- **[K3IMDB5](Embedding.Keras3/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](Embedding.Keras3/05-LSTM-Keras.ipynb)
Still the same problem, but with a network combining embedding and RNN, using Keras 3 and PyTorch
### Time series with Recurrent Neural Network (RNN), using Keras3/PyTorch
- **[K3LADYB1](RNN.Keras3/01-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](RNN.Keras3/01-Ladybug.ipynb)
Artificial dataset generation and prediction attempt via a recurrent network, using Keras 3 and PyTorch
### Unsupervised learning with an autoencoder neural network (AE), using Keras3
- **[K3AE1](AE.Keras3/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE.Keras3/01-Prepare-MNIST-dataset.ipynb)
Episode 1: Preparation of a noisy MNIST dataset
- **[K3AE2](AE.Keras3/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE.Keras3/02-AE-with-MNIST.ipynb)
Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.
- **[K3AE3](AE.Keras3/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE.Keras3/03-AE-with-MNIST-post.ipynb)
Episode 2 : Using the previously trained autoencoder to denoise data
- **[K3AE4](AE.Keras3/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE.Keras3/04-ExtAE-with-MNIST.ipynb)
Episode 4 : Construction of a denoiser and classifier model
- **[K3AE5](AE.Keras3/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE.Keras3/05-ExtAE-with-MNIST.ipynb)
Episode 5 : Construction of an advanced denoiser and classifier model
### Generative network with Variational Autoencoder (VAE), using Keras3
- **[K3VAE1](VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb)** - [First VAE, using functional API (MNIST dataset)](VAE.Keras3/01-VAE-with-MNIST-LossLayer.ipynb)
Construction and training of a VAE, using functional APPI, with a latent space of small dimension.
- **[K3VAE2](VAE.Keras3/02-VAE-with-MNIST.ipynb)** - [VAE, using a custom model class (MNIST dataset)](VAE.Keras3/02-VAE-with-MNIST.ipynb)
Construction and training of a VAE, using model subclass, with a latent space of small dimension.
- **[K3VAE3](VAE.Keras3/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE.Keras3/03-VAE-with-MNIST-post.ipynb)
Visualization and analysis of the VAE's latent space of the dataset MNIST
### Generative Adversarial Networks (GANs), using Lightning
- **[PLSHEEP3](DCGAN.Lightning/01-DCGAN-PL.ipynb)** - [A DCGAN to Draw a Sheep, using Pytorch Lightning](DCGAN.Lightning/01-DCGAN-PL.ipynb)
"Draw me a sheep", revisited with a DCGAN, using Pytorch Lightning
### Diffusion Model (DDPM) using PyTorch
- **[DDPM1](DDPM.PyTorch/01-ddpm.ipynb)** - [Fashion MNIST Generation with DDPM](DDPM.PyTorch/01-ddpm.ipynb)
Diffusion Model example, to generate Fashion MNIST images.
- **[DDPM2](DDPM.PyTorch/model.py)** - [DDPM Python classes](DDPM.PyTorch/model.py)
Python classes used by DDMP Example
### Training optimization, using PyTorch
- **[OPT1](Optimization.PyTorch/01-Apprentissages-rapides-et-Optimisations.ipynb)** - [Training setup optimization](Optimization.PyTorch/01-Apprentissages-rapides-et-Optimisations.ipynb)
The goal of this notebook is to go through a typical deep learning model training
### Deep Reinforcement Learning (DRL), using PyTorch
- **[DRL1](DRL.PyTorch/FIDLE_DQNfromScratch.ipynb)** - [Solving CartPole with DQN](DRL.PyTorch/FIDLE_DQNfromScratch.ipynb)
Using a a Deep Q-Network to play CartPole - an inverted pendulum problem (PyTorch)
- **[DRL2](DRL.PyTorch/FIDLE_rl_baselines_zoo.ipynb)** - [RL Baselines3 Zoo: Training in Colab](DRL.PyTorch/FIDLE_rl_baselines_zoo.ipynb)
### Miscellaneous things, but very important!
- **[NP1](Misc/00-Numpy.ipynb)** - [A short introduction to Numpy](Misc/00-Numpy.ipynb)
Numpy is an essential tool for the Scientific Python.
- **[ACTF1](Misc/01-Activation-Functions.ipynb)** - [Activation functions](Misc/01-Activation-Functions.ipynb)
Some activation functions, with their derivatives.
- **[PANDAS1](Misc/02-Using-pandas.ipynb)** - [Quelques exemples avec Pandas](Misc/02-Using-pandas.ipynb)
pandas is another essential tool for the Scientific Python.
- **[PYTORCH1](Misc/03-Using-Pytorch.ipynb)** - [Practical Lab : PyTorch](Misc/03-Using-Pytorch.ipynb)
PyTorch est l'un des principaux framework utilisé dans le Deep Learning
- **[TSB1](Misc/04-Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/04-Using-Tensorboard.ipynb)
4 ways to use Tensorboard from the Jupyter environment
- **[K3LSTM1](Misc/05-RNN.ipynb)** - [Basic Keras LSTM Layer](Misc/05-RNN.ipynb)
A small example of an LSTM layer in Keras
- **[PGRAD1](Misc/06-Gradients.ipynb)** - [Gradient illustration with PyTorch](Misc/06-Gradients.ipynb)
Exemple de calcul d'un gradient avec PyTorch
- **[FID1](Misc/99-Fid-Example.ipynb)** - [Exemple de notebook Fidle ](Misc/99-Fid-Example.ipynb)
Un simple exemple de notebook Fidle
Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
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See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).
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