updated with pr's merged to public repo since last release

Author: jyaistMap

guide/02-api-overview/release-notes.ipynb

@@ -20,7 +20,8 @@
    "source": [
     "### [`Guides`](https://developers.arcgis.com/python/guide/)\n",
     "* [Deep Learning with ArcGIS](https://developers.arcgis.com/python/guide/)\n",
-    "  * [Utilize Multipe GPUs to Train Model](https://developers.arcgis.com/python/guide/utilize-multiple-gpus-to-train-model)"
+    "  * [Utilize Multipe GPUs to Train Model](https://developers.arcgis.com/python/guide/utilize-multiple-gpus-to-train-model)\n",
+    "  * [Deep learning on time series data](https://developers.arcgis.com/python/guide/dl-on-time-series-data)"
    ]
   },
   {
@@ -29,7 +30,10 @@
    "source": [
     "### [`Samples`](https://developers.arcgis.com/python/sample-notebooks/)\n",
     "* [GIS Analysts and Data Scientists](https://developers.arcgis.com/python/sample-notebooks/)\n",
-    "  * [Streams extraction using ConnectNet ](https://developers.arcgis.com/python/sample-notebooks/streams-extraction-using-connectnet)"
+    "  * [Streams extraction using ConnectNet ](https://developers.arcgis.com/python/sample-notebooks/streams-extraction-using-connectnet)\n",
+    "  * [Streams extraction using Multi-Task Road Extractor](https://developers.arcgis.com/python/sample-notebooks/streams-extraction-using-multi-task-road-extractor)\n",
+    "  * [Forecasting monthly rainfall in California using Deep Learning Time Series techniques](https://developers.arcgis.com/python/sample-notebooks/forecasting-monthly-rainfall-in-california-using-deeplearning-timeseries-model-from-arcgis-learn)\n",
+    "  * [Landcover mapping using hyperspectral imagery and deep learning](https://developers.arcgis.com/python/sample-notebooks/landcover-classification-using-hyperspectral-imagery-and-deep-learning)"
    ]
   },
   {
@@ -148,7 +152,10 @@
     "* [GIS Analysts and Data Scientists](https://developers.arcgis.com/python/sample-notebooks/)\n",
     "  * Updates for `Try it live` Site:\n",
     "    * [Automate Road Surface Investigation Using Deep Learning](https://developers.arcgis.com/python/sample-notebooks/automate-road-surface-investigation-using-deep-learning/)\n",
-    "    * [Reconstructing 3D buildings from Aerial LiDAR with Deep Learning](https://developers.arcgis.com/python/sample-notebooks/building-reconstruction-using-mask-rcnn/)"
+    "    * [Reconstructing 3D buildings from Aerial LiDAR with Deep Learning](https://developers.arcgis.com/python/sample-notebooks/building-reconstruction-using-mask-rcnn/)\n",
+    "    * [Finding a New Home](https://developers.arcgis.com/python/sample-notebooks/finding-a-new-home/)\n",
+    "    * [Automate Building Footprint Extraction using Deep learning](https://developers.arcgis.com/python/sample-notebooks/automate-building-footprint-extraction-using-instance-segmentation/)\n",
+    "    * [Analyzing growth factors of rental properties in New York City](https://developers.arcgis.com/python/sample-notebooks/analyzing-growth-factors-of-airbnb-properties-in-new-york-city/)"
    ]
   },
   {
@@ -206,8 +213,9 @@
     "  * Fixes `fit()` method in segmentation models returning `NaN` values for certain attributes: [`UNetClassifier`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#unetclassifier), [`PSPNetClassifier`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#pspnetclassifier), [`DeepLab`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#deeplab)\n",
     "  * [`MultiTaskRoadExtractor`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#multitaskroadextractor) \n",
     "   * Fixes issue for [`show_results()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#arcgis.learn.MultiTaskRoadExtractor.show_results) plotting images incorrectly\n",
-    "   * Fixes issue causing [`save()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#arcgis.learn.MultiTaskRoadExtractor.save) only to work after calling [`fit()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#arcgis.learn.MultiTaskRoadExtractor.save)\n",
+    "   * Fixes issue causing [`save()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#arcgis.learn.MultiTaskRoadExtractor.save) only to work after calling [`fit()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#arcgis.learn.MultiTaskRoadExtractor.fit)\n",
     "   * Fixes [`load()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#arcgis.learn.MultiTaskRoadExtractor.load) so it only needs the model name\n",
+    "   * Fixes errors with [`fit()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html?highlight=retinanet#arcgis.learn.MultiTaskRoadExtractor.fit) when using monitor functionality\n",
     "* [`Unstructured Text Models`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#unstructured-text-models)\n",
     "  * [`arcgis.learn.text`](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#arcgis-learn-text-module) module\n",
     "    * Adds capability to use `HuggingFace` [pretrained models](https://huggingface.co/transformers/pretrained_models.html) for:\n",