Updating instructions for colab

FNALLPC · Jan 14, 2025 · df3ee93 · df3ee93
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commit df3ee93
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diff --git a/machine-learning-das/notebooks/1-datasets-uproot.ipynb b/machine-learning-das/notebooks/1-datasets-uproot.ipynb
@@ -13,6 +13,22 @@
     "Authors: Javier Duarte, Raghav Kansal\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/machine-learning-das/notebooks/2-boosted-decision-tree.ipynb b/machine-learning-das/notebooks/2-boosted-decision-tree.ipynb
@@ -13,6 +13,23 @@
     "Authors: Raghav Kansal"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot\n",
+    "!pip install xgboost"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/machine-learning-das/notebooks/3.1-dense-keras.ipynb b/machine-learning-das/notebooks/3.1-dense-keras.ipynb
@@ -13,6 +13,23 @@
     "Authors: Javier Duarte, Raghav Kansal"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot\n",
+    "!pip install xgboost"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/machine-learning-das/notebooks/3.2-dense-pytorch.ipynb b/machine-learning-das/notebooks/3.2-dense-pytorch.ipynb
@@ -13,6 +13,23 @@
     "Authors: Javier Duarte, Tyler Mitchell, Raghav Kansal"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot\n",
+    "!pip install xgboost"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/machine-learning-das/notebooks/3.3-dense-bayesian-optimization.ipynb b/machine-learning-das/notebooks/3.3-dense-bayesian-optimization.ipynb
diff --git a/machine-learning-das/notebooks/8-SetTransformer-PointCloud.ipynb b/machine-learning-das/notebooks/8-SetTransformer-PointCloud.ipynb
@@ -17,6 +17,7 @@
       },
       "source": [
         "# Jet Tagging with Permutation Invariance\n",
+        "Author: Abhijith Gandrakota, Jennifer Ngadiuba\n",
         "\n",
         "In this notebook we will see an implementation of the Transformer architecture for sets applied to the jet tagging task. For *sets* it is meant here a point cloud, i.e. a set of nodes without edges. We will instead use Multi-Head Attention to learn which nodes (or particles) have strong pair-wise interaction.\n",
         "\n",