final new guide changes

Author: vbhv14

guide/14-deep-learning/point_cloud_classification_using_point_transformer.ipynb

@@ -81,7 +81,7 @@
     "\n",
     "2. **Attention Mechanism:** PTv3 employs a simplified attention mechanism that is tailored for serialized point clouds. It utilizes a patch attention mechanism, grouping points into non-overlapping patches for localized processing.\n",
     "\n",
-    "3. **Positional Encoding:** PTv3 replaces the computationally expensive relative positional encoding with a simpler and more efficient conditional positional encoding (CPE). This is implemented by a sparse convolutional layer."
+    "3. **Positional Encoding:** PTv3 replaces the computationally expensive relative positional encoding with a simpler and more efficient conditional positional encoding (xCPE). This is implemented by a sparse convolutional layer."
    ]
   },
   {
@@ -149,7 +149,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Accurate positional information is crucial for point cloud understanding. PTv3 replaces the computationally expensive relative positional encoding (RPE) used in earlier versions with a simpler and more efficient approach. It utilizes a conditional positional encoding (CPE) implemented by a sparse convolutional layer.This CPE effectively captures positional information while minimizing computational overhead. The sparse convolutional layer is prepended before the attention layer with a skip connection, further enhancing the efficiency of the positional encoding process. The changes in positional encoding contribute to the overall efficiency and scalability of PTv3, enabling it to handle large-scale point cloud data with improved speed and accuracy."
+    "Accurate positional information is crucial for point cloud understanding. PTv3 replaces the computationally expensive relative positional encoding (RPE) used in earlier versions with a simpler and more efficient approach. It utilizes a conditional positional encoding (xCPE) implemented by a sparse convolutional layer. This xCPE effectively captures positional information while minimizing computational overhead. The sparse convolutional layer is prepended before the attention layer with a skip connection, further enhancing the efficiency of the positional encoding process. The changes in positional encoding contribute to the overall efficiency and scalability of PTv3, enabling it to handle large-scale point cloud data with improved speed and accuracy."
    ]
   },
   {

guide/14-deep-learning/point_cloud_object_detection_using_second.ipynb

@@ -343,9 +343,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:conda-arcgispro-py3-clone] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-conda-arcgispro-py3-clone-py"
   },
   "language_info": {
    "codemirror_mode": {