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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
parent ba6bea2c73
commit 632240d232
2 changed files with 8 additions and 4 deletions
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10
M2/Deep Learning/TP2/TP2 - Starter.ipynb
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@@ -15,7 +15,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -24,7 +24,9 @@
"\n", "\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import seaborn as sns; sns.set(style='whitegrid')\n", "import seaborn as sns\n",
"\n",
"sns.set(style='whitegrid')\n",
"\n", "\n",
"import tensorflow as tf\n", "import tensorflow as tf\n",
"from tensorflow import keras\n", "from tensorflow import keras\n",
@@ -247,7 +249,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "studies",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@@ -261,7 +263,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.6" "version": "3.13.3"
} }
}, },
"nbformat": 4, "nbformat": 4,

2
README.md
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@@ -30,6 +30,7 @@ The projects are organized into two main sections:
- `M2` - `M2`
- `Data Visualisation` - `Data Visualisation`
- `Deep Learning`
- `Linear Models` - `Linear Models`
- `Machine Learning` - `Machine Learning`
- `Risks Management` - `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. - [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. - [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. - [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. - [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. - [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. - [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.