Updates to items_metadata and minor fixes

Updates to items_metadata and minor fixes for v184

Author: mohi9282

guide/14-deep-learning/how-sequencetosequence-works.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How SequenceToSequence works"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -838,9 +845,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "deep-learning",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "deep-learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -852,7 +859,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,

items_metadata.yaml

@@ -683,7 +683,76 @@ samples:
   licenseInfo: ""
   runtime: advanced_gpu
   tags: ["Data Science", "GIS", "Building", "Classification", "Deep Learning"]
-
+- title: Address Standardization and Correction using SequenceToSequence model
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=e8e2b0ac54584d079420b35b011d3d50
+  path: ./samples/04_gis_analysts_data_scientists/address-standardization-and-correction-with-sequencetosequence.ipynb
+  thumbnail: ./static/thumbnails/address_standardization.jpg
+  snippet: Train a model using SequenceToSequence class of arcgis.learn.text module to translate the non-standard and erroneous address to their standard and correct form.
+  description: Train a model using SequenceToSequence class of arcgis.learn.text module to translate the non-standard and erroneous address to their standard and correct form.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Text", "Classification", "Deep Learning"]
+- title: Using and updating GIS content
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=46a02bf3fe8546ec9ec4ffb230b94059
+  path: ./samples/05_content_publishers/using_and_updating_GIS_content.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: This sample shows you how to update the content of web maps and web scenes.
+  description: This sample shows you how to update the content of web maps and web scenes.
+  licenseInfo: ""
+  tags: ["Data Science", "GIS", "Text", "Classification"]
+- title: Updating features in a feature layer
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=e8e2b0ac54584d079420b35b011d3d50
+  path: ./samples/05_content_publishers/updating_features_in_a_feature_layer.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
+  description: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
+  licenseInfo: ""
+  tags: ["Data Science", "GIS", "Text", "Classification"]
+- title: Land Parcel Extraction using Edge Detection model
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=e164250b748240b5909159602dee826a
+  path: ./samples/04_gis_analysts_data_scientists/land_parcel_extraction_using_edge_detection_deep_learning_model.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: This sample shows how ArcGIS API for Python can be used to train a deep learning edge detection model to extract parcels from satellite imagery.
+  description: This sample shows how ArcGIS API for Python can be used to train a deep learning edge detection model to extract parcels from satellite imagery.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Edge Detection", "Land Parcel", "Deep Learning"]
+- title: Change Detection of Buildings from Satellite Imagery
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=d700b1713cbf404bad01d92dfca0c91b
+  path: ./samples/04_gis_analysts_data_scientists/change_detection_of_buildings_from_satellite_imagery.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: ChangeDetector is used to identify areas of persistent change between two different time periods using remotely sensed images.
+  description: ChangeDetector is used to identify areas of persistent change between two different time periods using remotely sensed images.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Change Detection", "Deep Learning"]
+- title: Generating rgb imagery from digital surface model using Pix2Pix
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=d2d58e9d0e624f4baddd983d8acea3da
+  path: ./samples/04_gis_analysts_data_scientists/generating_rgb_imagery_from_digital_surface_model_using_pix2pix.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
+  description: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Image Translation", "Deep Learning"]
+- title: Coastline extraction using Landsat-8 multispectral imagery and band ratio technique
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=4d8d5789a5e045bbbd02a01903841439
+  path: ./samples/04_gis_analysts_data_scientists/coastline_extraction-usa-landsat8_multispectral_imagery.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: Landsat-8 multispectral imagery is used in the current study as it covers a wavelength ranging from 0.43 to 12.51 micrometers, and hence suitable for coastal and aerosol studies.
+  description: Landsat-8 multispectral imagery is used in the current study as it covers a wavelength ranging from 0.43 to 12.51 micrometers, and hence suitable for coastal and aerosol studies.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Coastline Extraction", "Imagery"]
+- title: Translating Story Map from one language to another using Deep Learning
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=15a731751bad43c7abf7258b7aa90bac
+  path: ./samples/04_gis_analysts_data_scientists/translating_story_map_from_one_language_to_another.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: In this notebook, we will pick a story map written in English language, and create another story map with the text translated to Spanish language using the arcgis.learn.text's TextTranslator class.
+  description: In this notebook, we will pick a story map written in English language, and create another story map with the text translated to Spanish language using the arcgis.learn.text's TextTranslator class.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Story Map", "Text Translation", "Deep Learning"]
 guides: []
 labs:
 - title: Create Data

samples/04_gis_analysts_data_scientists/address-standardization-and-correction-with-sequencetosequence.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Address Standardization and Correction using SequenceToSequence model"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -913,9 +920,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "deep-learning",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "deep-learning"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -927,7 +934,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,