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

M2/Deep Learning/TP2/TP2 - Starter.ipynb

@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,9 @@
     "\n",
     "%matplotlib inline\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",
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
@@ -247,7 +249,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "studies",
    "language": "python",
    "name": "python3"
   },
@@ -261,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,

README.md

@@ -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.