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Ajoute une section "Deep Learning" au README et met à jour les dépendances pour inclure Keras

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Arthur Danjou
2025-11-05 17:15:53 +01:00
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README.md
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@@ -30,6 +30,7 @@ The projects are organized into two main sections:
- `M2`
- `Data Visualisation`
- `Deep Learning`
- `Linear Models`
- `Machine Learning`
- `Risks Management`
@@ -45,6 +46,7 @@ The projects are organized into two main sections:
- [SciPy](https://www.scipy.org): A library for advanced scientific computations including optimization, integration, and signal processing.
- [Scikit-learn](https://scikit-learn.org): A robust library offering simple and efficient tools for machine learning and statistical modeling, including classification, regression, and clustering.
- [TensorFlow](https://www.tensorflow.org): A comprehensive open-source framework for building and deploying machine learning and deep learning models.
- [Keras](https://keras.io): A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation.
- [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python.
- [Plotly](https://plotly.com): An interactive graphing library for creating dynamic visualizations in Python and R.
- [Seaborn](https://seaborn.pydata.org): A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive and informative graphics.