├── .gitignore ├── Dockerfile ├── LICENSE.txt ├── README.md ├── formation ├── 00-Programme de la formation │ ├── 00-Programme de la formation.md │ ├── 00-Programme de la formation.pdf │ └── Images │ │ ├── logo.PNG │ │ └── logo2.PNG ├── 01-Introduction au machine learning │ ├── 01-Introduction au Machine Learning.md │ ├── 01-Introduction au Machine Learning.pdf │ └── Images │ │ ├── 02-intro_ML │ │ ├── IA_ML_DL.png │ │ ├── Reg_Clas.PNG │ │ ├── Types_de_learning.PNG │ │ ├── convergence.PNG │ │ ├── convergence2.PNG │ │ ├── descente_gradient.PNG │ │ ├── entrainement.PNG │ │ ├── entrainement_test.PNG │ │ ├── entrainement_test_valid.PNG │ │ ├── epoch.PNG │ │ ├── fonction_cout.PNG │ │ ├── formule1.PNG │ │ ├── formule2.PNG │ │ ├── formule3.PNG │ │ ├── formule4.PNG │ │ ├── full_batch_gradient_descent.PNG │ │ ├── gradient.PNG │ │ ├── learning_rate.PNG │ │ ├── overfitting.PNG │ │ ├── regression_lineaire.PNG │ │ ├── scikitlearn1.PNG │ │ ├── scikitlearn2.PNG │ │ ├── stochastic_gradient_descent.PNG │ │ ├── sur_sous_apprentissage.PNG │ │ ├── sur_sous_apprentissage2.PNG │ │ └── types_apprentissages.PNG │ │ ├── logo.PNG │ │ └── logo2.PNG ├── 02-Machine learning avec Pandas et Scikit-learn │ ├── 2.1 Manipulation et description des donnees.ipynb │ ├── 2.2 Algorithmes de machine learning.ipynb │ ├── 2.3 Evaluation des modeles.ipynb │ ├── 2.4.1 Forets d'arbre aleatoire.ipynb │ ├── 2.4.2 Clustering - KMeans.ipynb │ ├── fig_code │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── data.cpython-36.pyc │ │ │ ├── data.cpython-37.pyc │ │ │ ├── figures.cpython-36.pyc │ │ │ ├── figures.cpython-37.pyc │ │ │ ├── helpers.cpython-36.pyc │ │ │ ├── helpers.cpython-37.pyc │ │ │ ├── linear_regression.cpython-36.pyc │ │ │ ├── linear_regression.cpython-37.pyc │ │ │ └── sgd_separator.cpython-36.pyc │ │ ├── data.py │ │ ├── figures.py │ │ ├── helpers.py │ │ └── linear_regression.py │ └── images │ │ └── Iris_dataset_scatterplot.svg.png ├── 03-Introduction au DL │ ├── 02-Introduction au deep learning.md │ ├── 02-Introduction au deep learning.pdf │ └── Images │ │ ├── 04-intro_DL │ │ ├── DL.PNG │ │ ├── approche_courbe_segments.png │ │ ├── classif.PNG │ │ ├── convolution.PNG │ │ ├── convolution2.PNG │ │ ├── dropout.PNG │ │ ├── fonction_activation.PNG │ │ ├── layers.PNG │ │ ├── max_pooling.png │ │ ├── neurone.png │ │ ├── neurone0.PNG │ │ ├── réseau_complet.PNG │ │ └── sigmoid_relu.PNG │ │ ├── logo.PNG │ │ └── logo2.PNG ├── 04-La librairie Pytorch │ ├── Exercices Pytorch.ipynb │ ├── LaLibrairiePytorch.pdf │ └── images │ │ ├── exo1.PNG │ │ ├── exo2.PNG │ │ └── exo4.PNG ├── TP-01-Python pour Data Scientists │ ├── Images │ │ ├── 03-python_data_science │ │ │ ├── code_pandas.PNG │ │ │ ├── code_pandas2.PNG │ │ │ ├── code_pandas3.PNG │ │ │ ├── code_pandas4.PNG │ │ │ ├── diagramme.PNG │ │ │ ├── librairies.PNG │ │ │ ├── pandas.PNG │ │ │ ├── part_marché.PNG │ │ │ ├── sklearn.PNG │ │ │ ├── sklearn_code.PNG │ │ │ ├── sklearn_code1.PNG │ │ │ └── sklearn_code2.PNG │ │ ├── logo.PNG │ │ └── logo2.PNG │ ├── Titanic-Correction.ipynb │ ├── Titanic.ipynb │ ├── housing.csv │ ├── ml_housing-Correction.ipynb │ ├── ml_housing.ipynb │ └── titanic │ │ ├── README.md │ │ ├── test.csv │ │ └── train.csv ├── TP-02-Reconnaissance de chiffres manuscrits │ ├── ML_mnist.ipynb │ └── ML_mnist_correction.ipynb ├── TP-03-Réseaux de neurones │ ├── mnist_pytorch.ipynb │ └── mnist_pytorch_correction.ipynb └── data │ ├── calendar_extrait.csv │ ├── credit.xlsx │ ├── employee-earnings-report-2017.csv │ ├── listing_extrait.csv │ └── salaries.sqlite └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | .ipynb_checkpoints 3 | *.swp 4 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/meteofrance/formation-machine-learning/HEAD/Dockerfile 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