About Fidle

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

For more information, see https://fidle.cnrs.fr :

• Fidle site
• Presentation of the training
• Detailed program
• Subscribe to the list, to stay informed !
• Corrected notebooks
• Follow us on our channel :
  

For more information, you can contact us at :

Current Version : 3.0.1

Course materials

Course slides The course in pdf format	Notebooks     Get a Zip or clone this repository	Datasets All the needed datasets	Videos     Our Youtube channel

Have a look about How to get and install these notebooks and datasets.

Jupyter notebooks

Linear and logistic regression

• LINR1 - Linear regression with direct resolution
  Low-level implementation, using numpy, of a direct resolution for a linear regression
• GRAD1 - Linear regression with gradient descent
  Low level implementation of a solution by gradient descent. Basic and stochastic approach.
• POLR1 - Complexity Syndrome
  Illustration of the problem of complexity with the polynomial regression
• LOGR1 - Logistic regression
  Simple example of logistic regression with a sklearn solution

Perceptron Model 1957

• PER57 - Perceptron Model 1957
  Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !

BHPD regression (DNN), using Keras3/PyTorch

• K3BHPD1 - Regression with a Dense Network (DNN)
  Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
• K3BHPD2 - Regression with a Dense Network (DNN) - Advanced code
  A more advanced implementation of the precedent example, using Keras3

BHPD regression (DNN), using PyTorch

• PBHPD1 - Regression with a Dense Network (DNN)
  A Simple regression with a Dense Neural Network (DNN) using Pytorch - BHPD dataset

Wine Quality prediction (DNN), using Keras3/PyTorch

• K3WINE1 - Wine quality prediction with a Dense Network (DNN)
  Another example of regression, with a wine quality prediction, using Keras 3 and PyTorch

Wine Quality prediction (DNN), using PyTorch/Lightning

• LWINE1 - Wine quality prediction with a Dense Network (DNN)
  Another example of regression, with a wine quality prediction, using PyTorch Lightning

MNIST classification (DNN,CNN), using Keras3/PyTorch

• K3MNIST1 - Simple classification with DNN
  An example of classification using a dense neural network for the famous MNIST dataset
• K3MNIST2 - Simple classification with CNN
  An example of classification using a convolutional neural network for the famous MNIST dataset

MNIST classification (DNN,CNN), using PyTorch

• PMNIST1 - Simple classification with DNN
  Example of classification with a fully connected neural network, using Pytorch

MNIST classification (DNN,CNN), using PyTorch/Lightning

• LMNIST2 - Simple classification with DNN
  An example of classification using a dense neural network for the famous MNIST dataset, using PyTorch Lightning
• LMNIST2 - Simple classification with CNN
  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 - Dataset analysis and preparation
  Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
• K3GTSRB2 - First convolutions
  Episode 2 : First convolutions and first classification of our traffic signs, using Keras3
• K3GTSRB3 - Training monitoring
  Episode 3 : Monitoring, analysis and check points during a training session, using Keras3
• K3GTSRB4 - Hight level example (Keras-cv)
  An example of using a pre-trained model with Keras-cv
• K3GTSRB10 - OAR batch script submission
  Bash script for an OAR batch submission of an ipython code
• K3GTSRB11 - SLURM batch script
  Bash script for a Slurm batch submission of an ipython code

Sentiment analysis with word embedding, using Keras3/PyTorch

• K3IMDB1 - Sentiment analysis with hot-one encoding
  A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB), using Keras 3 on PyTorch
• K3IMDB2 - Sentiment analysis with text embedding
  A very classical example of word embedding with a dataset from Internet Movie Database (IMDB), using Keras 3 on PyTorch
• K3IMDB3 - Reload and reuse a saved model
  Retrieving a saved model to perform a sentiment analysis (movie review), using Keras 3 and PyTorch
• K3IMDB4 - Reload embedded vectors
  Retrieving embedded vectors from our trained model, using Keras 3 and PyTorch
• K3IMDB5 - Sentiment analysis with a RNN network
  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 - Prediction of a 2D trajectory via RNN
  Artificial dataset generation and prediction attempt via a recurrent network, using Keras 3 and PyTorch

Sentiment analysis with transformer, using PyTorch

• TRANS1 - IMDB, Sentiment analysis with Transformers
  Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version
• TRANS2 - IMDB, Sentiment analysis with Transformers
  Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version

Diffusion Model (DDPM) using PyTorch

• DDPM1 - Fashion MNIST Generation with DDPM
  Diffusion Model example, to generate Fashion MNIST images.
• DDPM2 - DDPM Python classes
  Python classes used by DDMP Example

Training optimization, using PyTorch

• OPT1 - Training setup optimization
  The goal of this notebook is to go through a typical deep learning model training

Deep Reinforcement Learning (DRL), using PyTorch

• DRL1 - Solving CartPole with DQN
  Using a a Deep Q-Network to play CartPole - an inverted pendulum problem (PyTorch)
• DRL2 - RL Baselines3 Zoo: Training in Colab
  Demo of Stable baseline3 with Colab

Miscellaneous things, but very important!

• NP1 - A short introduction to Numpy
  Numpy is an essential tool for the Scientific Python.
• ACTF1 - Activation functions
  Some activation functions, with their derivatives.
• PANDAS1 - Quelques exemples avec Pandas
  pandas is another essential tool for the Scientific Python.
• PYTORCH1 - Practical Lab : PyTorch
  PyTorch est l'un des principaux framework utilisé dans le Deep Learning
• TSB1 - Tensorboard with/from Jupyter
  4 ways to use Tensorboard from the Jupyter environment
• SCRATCH1 - Scratchbook
  A scratchbook for small examples

Installation

Have a look about How to get and install these notebooks and datasets.

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.
See Disclaimer.

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