Merge pull request #2449 from Esri/fix_deep_learning_guides_links

fixed broken links in 3 guides

Author: priyankatuteja

guide/14-deep-learning/add_model_using_model_extension.ipynb

@@ -608,22 +608,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From here, we can continue with the usual workflow of using an `arcgis.learn` deep learning model. Refer to the [Detecting Swimming Pools using Deep Learning sample](https://developers.arcgis.com/python/sample-notebooks/detecting-swimming-pools-using-satellite-image-and-deep-learning/) sample notebook to see the workflow for an **object detection** model and the [Land Cover Classification using Satellite Imagery and Deep Learning](https://developers.arcgis.com/python/sample-notebooks/land-cover-classification-using-unet/) sample notebook for the workflow for a **pixel classification** model."
+    "From here, we can continue with the usual workflow of using an `arcgis.learn` deep learning model. Refer to the [Detecting Swimming Pools using Deep Learning sample](https://developers.arcgis.com/python/samples/detecting-swimming-pools-using-satellite-image-and-deep-learning/) sample notebook to see the workflow for an **object detection** model and the [Land Cover Classification using Satellite Imagery and Deep Learning](https://developers.arcgis.com/python/samples/land-cover-classification-using-unet/) sample notebook for the workflow for a **pixel classification** model."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:conda-new] *",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-conda-new-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -635,7 +633,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.12.11"
   },
   "toc": {
    "base_numbering": 1,

guide/14-deep-learning/how-named-entity-recognition-works.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Named Entity Extraction Workflow with `arcgis.learn`"
+    "# Named Entity Extraction Workflow with arcgis.learn"
    ]
   },
   {
@@ -91,7 +91,7 @@
     "- Refer to the section [Install deep learning dependencies of arcgis.learn module](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-deep-learning-dependencies) for detailed explanation about deep learning dependencies.\n",
     "- **Labeled data**: For `EntityRecognizer` to learn, it needs to see examples that have been labeled for all the custom categories that the model is expected to extract. Head to the **Data preparation** section to see the supported formats for training data.\n",
     "\n",
-    "- If you wish to try this workflow, you can find a sample notebook along with the necessary labeled training and test datasets over [here](https://developers.arcgis.com/python/sample-notebooks/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning#Publishing-the-results-as-feature-layer)."
+    "- If you wish to try this workflow, you can find a sample notebook along with the necessary labeled training and test datasets over [here](https://developers.arcgis.com/python/samples/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning#Publishing-the-results-as-feature-layer)."
    ]
   },
   {
@@ -1482,7 +1482,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,

guide/14-deep-learning/use_mmdetection_with_arcgis_learn.ipynb

@@ -109,9 +109,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "30813304",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -212,7 +210,7 @@
    "id": "22976ebb",
    "metadata": {},
    "source": [
-    "For more information about the API, visit the [API reference for MMDetection](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#mmdetection). For a detailed object detection workflow, refer to a sample [notebook](https://developers.arcgis.com/python/sample-notebooks/detecting-and-categorizing-brick-kilns-from-satellite-imagery/)."
+    "For more information about the API, visit the [API reference for MMDetection](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#mmdetection). For a detailed object detection workflow, refer to a [sample notebook](https://developers.arcgis.com/python/samples/detecting-and-categorizing-brick-kilns-from-satellite-imagery/)."
    ]
   },
   {
@@ -248,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.12.11"
   },
   "toc": {
    "base_numbering": 1,