add nb for detreg

Author: priyankatuteja

samples/04_gis_analysts_data_scientists/detecting_palm_trees_using_deep_learning.ipynb

@@ -58,7 +58,7 @@
    "id": "681bbbbc",
    "metadata": {},
    "source": [
-    "Accurate and high-resolution imagery is essential when extracting features. The model will only be able to identify the palm trees if the pixel size is small enough to distinguish palm canopies. Additionally, to calculate tree health, you'll need an image with spectral bands that will enable you to generate a vegetation health index. You'll find and download the imagery for this study from OpenAerialMap, an open-source repository of high-resolution, multispectral imagery.\n",
+    "Accurate and high-resolution imagery is essential when extracting features. The model will only be able to identify the palm trees if the pixel size is small enough to distinguish palm canopies. Additionally, to calculate tree health, we'll need an image with spectral bands that will enable you to generate a vegetation health index. You'll find and download the imagery for this study from OpenAerialMap, an open-source repository of high-resolution, multispectral imagery.\n",
     "\n",
     "- Go to the [OpenAerialMap website](https://openaerialmap.org/).\n",
     "\n",
@@ -1323,7 +1323,7 @@
    "id": "27b740d8",
    "metadata": {},
    "source": [
-    "The bulk of the work in extracting features from imagery is preparing the data, creating training samples, and training the model. Now that these steps have been completed, we'll use a trained model to detect palm trees throughout your imagery. Object detection is a process that typically requires multiple tests to achieve the best results. There are several parameters that you can alter to allow your model to perform best. To test these parameters quickly, we'll try detecting trees in a small section of the image. Once you're satisfied with the results, we'll extend the detection tools to the full image."
+    "The bulk of the work in extracting features from imagery is preparing the data, creating training samples, and training the model. Now that these steps have been completed, we'll use the [trained model]() to detect palm trees throughout your imagery. Object detection is a process that typically requires multiple tests to achieve the best results. There are several parameters that you can alter to allow your model to perform best. To test these parameters quickly, we'll try detecting trees in a small section of the image. Once you're satisfied with the results, we'll extend the detection tools to the full image."
    ]
   },
   {