Changed "developed on" to "developed_on"; updated make_readme.py; udpated README and index (#294)

Author: jdesanto

notebook_examples/README.md

@@ -12,7 +12,7 @@ The ADS SDK can be downloaded from [PyPi](https://pypi.org/project/oracle-ads/),
 
 
 ## Topics
-<img src="https://img.shields.io/badge/deploy model-7-brightgreen"> <img src="https://img.shields.io/badge/register model-7-brightgreen"> <img src="https://img.shields.io/badge/train model-7-brightgreen"> <img src="https://img.shields.io/badge/data flow-4-brightgreen"> <img src="https://img.shields.io/badge/pyspark-4-brightgreen"> <img src="https://img.shields.io/badge/automlx-4-brightgreen"> <img src="https://img.shields.io/badge/oracle open data-3-brightgreen"> <img src="https://img.shields.io/badge/bds-3-brightgreen"> <img src="https://img.shields.io/badge/scikit learn-2-brightgreen"> <img src="https://img.shields.io/badge/big data service-2-brightgreen"> <img src="https://img.shields.io/badge/text classification-2-brightgreen"> <img src="https://img.shields.io/badge/nlp-2-brightgreen"> <img src="https://img.shields.io/badge/regression-2-brightgreen"> <img src="https://img.shields.io/badge/language services-2-brightgreen"> <img src="https://img.shields.io/badge/string manipulation-2-brightgreen"> <img src="https://img.shields.io/badge/regex-2-brightgreen"> <img src="https://img.shields.io/badge/regular expression-2-brightgreen"> <img src="https://img.shields.io/badge/natural language processing-2-brightgreen"> <img src="https://img.shields.io/badge/NLP-2-brightgreen"> <img src="https://img.shields.io/badge/part of speech tagging-2-brightgreen"> <img src="https://img.shields.io/badge/named entity recognition-2-brightgreen"> <img src="https://img.shields.io/badge/sentiment analysis-2-brightgreen"> <img src="https://img.shields.io/badge/custom plugins-2-brightgreen"> <img src="https://img.shields.io/badge/data catalog metastore-2-brightgreen"> <img src="https://img.shields.io/badge/xgboost-2-brightgreen"> <img src="https://img.shields.io/badge/classification-2-brightgreen"> <img src="https://img.shields.io/badge/autonomous database-2-brightgreen"> <img src="https://img.shields.io/badge/intel-1-brightgreen"> <img src="https://img.shields.io/badge/intel extension-1-brightgreen"> <img src="https://img.shields.io/badge/scikit learn-1-brightgreen"> 
+<img src="https://img.shields.io/badge/deploy model-7-brightgreen"> <img src="https://img.shields.io/badge/register model-7-brightgreen"> <img src="https://img.shields.io/badge/train model-7-brightgreen"> <img src="https://img.shields.io/badge/pyspark-4-brightgreen"> <img src="https://img.shields.io/badge/data flow-4-brightgreen"> <img src="https://img.shields.io/badge/automlx-4-brightgreen"> <img src="https://img.shields.io/badge/bds-3-brightgreen"> <img src="https://img.shields.io/badge/oracle open data-3-brightgreen"> <img src="https://img.shields.io/badge/scikit learn-2-brightgreen"> <img src="https://img.shields.io/badge/big data service-2-brightgreen"> <img src="https://img.shields.io/badge/language services-2-brightgreen"> <img src="https://img.shields.io/badge/string manipulation-2-brightgreen"> <img src="https://img.shields.io/badge/regex-2-brightgreen"> <img src="https://img.shields.io/badge/regular expression-2-brightgreen"> <img src="https://img.shields.io/badge/natural language processing-2-brightgreen"> <img src="https://img.shields.io/badge/NLP-2-brightgreen"> <img src="https://img.shields.io/badge/part of speech tagging-2-brightgreen"> <img src="https://img.shields.io/badge/named entity recognition-2-brightgreen"> <img src="https://img.shields.io/badge/sentiment analysis-2-brightgreen"> <img src="https://img.shields.io/badge/custom plugins-2-brightgreen"> <img src="https://img.shields.io/badge/autonomous database-2-brightgreen"> <img src="https://img.shields.io/badge/classification-2-brightgreen"> <img src="https://img.shields.io/badge/text classification-2-brightgreen"> <img src="https://img.shields.io/badge/regression-2-brightgreen"> <img src="https://img.shields.io/badge/xgboost-2-brightgreen"> <img src="https://img.shields.io/badge/nlp-2-brightgreen"> <img src="https://img.shields.io/badge/data catalog metastore-2-brightgreen"> <img src="https://img.shields.io/badge/model-1-brightgreen"> <img src="https://img.shields.io/badge/model experiments-1-brightgreen"> <img src="https://img.shields.io/badge/model version set-1-brightgreen"> 
 
 ## Contents
  - [Audi Autonomous Driving Dataset Repository](#audi-autonomous_driving-oracle_open_data.ipynb)
@@ -56,41 +56,41 @@ The ADS SDK can be downloaded from [PyPi](https://pypi.org/project/oracle-ads/),
 
 
 ## Notebooks
-### <a name="automlx-anomaly_detection.ipynb"></a> - Building and Explaining an Anomaly Detector using AutoMLx - Experimental
+### <a name="automlx-classifier.ipynb"></a> - Building and Explaining a Classifier using AutoMLx
 
-<sub>Updated: 03/17/2023</sub>
-#### [`automlx-anomaly_detection.ipynb`](automlx-anomaly_detection.ipynb)
+<sub>Updated: 03/26/2023</sub>
+#### [`automlx-classifier.ipynb`](automlx-classifier.ipynb)
 
 
-Build an anomaly detection model using the experimental, fully unsupervised anomaly detection pipeline in Oracle AutoMLx for the public Credit Card Fraud dataset.
+Build a classifier using the Oracle AutoMLx tool and binary data set of Census income data.
 
 This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
 
 
-`automlx`  `anomaly detection`
+`automlx`  `classification`  `classifier`
 
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="automlx-classifier.ipynb"></a> - Building and Explaining a Classifier using AutoMLx
+### <a name="automlx-text_classification.ipynb"></a> - Building and Explaining a Text Classifier using AutoMLx
 
-<sub>Updated: 03/17/2023</sub>
-#### [`automlx-classifier.ipynb`](automlx-classifier.ipynb)
+<sub>Updated: 03/26/2023</sub>
+#### [`automlx-text_classification.ipynb`](automlx-text_classification.ipynb)
 
 
-Build a classifier using the Oracle AutoMLx tool and binary data set of Census income data.
+build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset
 
 This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
 
 
-`automlx`  `classification`  `classifier`
+`automlx`  `text classification`  `text classifier`
 
-<sub>Universal Permissive License v 1.0</sup>
+<sub>Universal Permissive License v 1.0.</sup>
 
 ---
 ### <a name="automlx-regression.ipynb"></a> - Building and Explaining a Regressor using AutoMLx
 
-<sub>Updated: 03/17/2023</sub>
+<sub>Updated: 03/26/2023</sub>
 #### [`automlx-regression.ipynb`](automlx-regression.ipynb)
 
 
@@ -104,20 +104,20 @@ This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="automlx-text_classification.ipynb"></a> - Building and Explaining a Text Classifier using AutoMLx
+### <a name="automlx-anomaly_detection.ipynb"></a> - Building and Explaining an Anomaly Detector using AutoMLx - Experimental
 
-<sub>Updated: 03/17/2023</sub>
-#### [`automlx-text_classification.ipynb`](automlx-text_classification.ipynb)
+<sub>Updated: 03/26/2023</sub>
+#### [`automlx-anomaly_detection.ipynb`](automlx-anomaly_detection.ipynb)
 
 
-build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset
+Build an anomaly detection model using the experimental, fully unsupervised anomaly detection pipeline in Oracle AutoMLx for the public Credit Card Fraud dataset.
 
 This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
 
 
-`automlx`  `text classification`  `text classifier`
+`automlx`  `anomaly detection`
 
-<sub>Universal Permissive License v 1.0.</sup>
+<sub>Universal Permissive License v 1.0</sup>
 
 ---
 ### <a name="audi-autonomous_driving-oracle_open_data.ipynb"></a> - Audi Autonomous Driving Dataset Repository
@@ -250,7 +250,7 @@ This notebook was developed on the conda pack with slug: `pyspark30_p37_cpu_v5`
 ---
 ### <a name="train-register-deploy-other-frameworks.ipynb"></a> - Train, Register, and Deploy a Generic Model
 
-<sub>Updated: 12/22/2022</sub>
+<sub>Updated: 03/18/2023</sub>
 #### [`train-register-deploy-other-frameworks.ipynb`](train-register-deploy-other-frameworks.ipynb)
 
 
@@ -266,7 +266,7 @@ This notebook was developed on the conda pack with slug: `generalml_p38_cpu_v1`
 ---
 ### <a name="train-register-deploy-huggingface-pipeline.ipynb"></a> - Train, register, and deploy HuggingFace Pipeline
 
-<sub>Updated: 03/06/2023</sub>
+<sub>Updated: 03/18/2023</sub>
 #### [`train-register-deploy-huggingface-pipeline.ipynb`](train-register-deploy-huggingface-pipeline.ipynb)
 
 
@@ -328,31 +328,31 @@ This notebook was developed on the conda pack with slug: `pyspark30_p37_cpu_v5`
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="natural_language_processing.ipynb"></a> - Natural Language Processing
+### <a name="automlx-forecasting.ipynb"></a> - Building a Forecaster using AutoMLx
 
-<sub>Updated: 12/22/2022</sub>
-#### [`natural_language_processing.ipynb`](natural_language_processing.ipynb)
+<sub>Updated: 03/26/2023</sub>
+#### [`automlx-forecasting.ipynb`](automlx-forecasting.ipynb)
 
 
-Use the ADS SDK to process and manipulate strings. This notebook includes regular expression matching and natural language (NLP) parsing, including part-of-speech tagging, named entity recognition, and sentiment analysis. It also shows how to create and use custom plugins specific to your specific needs.
+Use Oracle AutoMLx to build a forecast model with real-world data sets.
 
-This notebook was developed on the conda pack with slug: `nlp_p37_cpu_v2`
+This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
 
 
 `language services`  `string manipulation`  `regex`  `regular expression`  `natural language processing`  `NLP`  `part-of-speech tagging`  `named entity recognition`  `sentiment analysis`  `custom plugins`
 
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="automlx-forecasting.ipynb"></a> - Building a Forecaster using AutoMLx
+### <a name="natural_language_processing.ipynb"></a> - Natural Language Processing
 
-<sub>Updated: 03/17/2023</sub>
-#### [`automlx-forecasting.ipynb`](automlx-forecasting.ipynb)
+<sub>Updated: 12/22/2022</sub>
+#### [`natural_language_processing.ipynb`](natural_language_processing.ipynb)
 
 
-Use Oracle AutoMLx to build a forecast model with real-world data sets.
+Use the ADS SDK to process and manipulate strings. This notebook includes regular expression matching and natural language (NLP) parsing, including part-of-speech tagging, named entity recognition, and sentiment analysis. It also shows how to create and use custom plugins specific to your specific needs.
 
-This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
+This notebook was developed on the conda pack with slug: `nlp_p37_cpu_v2`
 
 
 `language services`  `string manipulation`  `regex`  `regular expression`  `natural language processing`  `NLP`  `part-of-speech tagging`  `named entity recognition`  `sentiment analysis`  `custom plugins`
@@ -362,7 +362,7 @@ This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
 ---
 ### <a name="train-register-deploy-lightgbm.ipynb"></a> - Train, Register, and Deploy a LightGBM Model
 
-<sub>Updated: 12/22/2022</sub>
+<sub>Updated: 03/18/2023</sub>
 #### [`train-register-deploy-lightgbm.ipynb`](train-register-deploy-lightgbm.ipynb)
 
 
@@ -474,7 +474,7 @@ This notebook was developed on the conda pack with slug: `generalml_p38_cpu_v1`
 ---
 ### <a name="pypgx-graph_analytics-machine_learning.ipynb"></a> - Graph Analytics and Graph Machine Learning with PyPGX
 
-<sub>Updated: 03/13/2023</sub>
+<sub>Updated: 03/17/2023</sub>
 #### [`pypgx-graph_analytics-machine_learning.ipynb`](pypgx-graph_analytics-machine_learning.ipynb)
 
 
@@ -538,7 +538,7 @@ This notebook was developed on the conda pack with slug: `pyspark32_p38_cpu_v1`
 ---
 ### <a name="train-register-deploy-pytorch.ipynb"></a> - Train, Register, and Deploy a PyTorch Model
 
-<sub>Updated: 02/10/2023</sub>
+<sub>Updated: 03/18/2023</sub>
 #### [`train-register-deploy-pytorch.ipynb`](train-register-deploy-pytorch.ipynb)
 
 
@@ -554,7 +554,7 @@ This notebook was developed on the conda pack with slug: `pytorch110_p38_cpu_v1`
 ---
 ### <a name="train-register-deploy-sklearn.ipynb"></a> - Train, register, and deploy Sklearn Model
 
-<sub>Updated: 12/22/2022</sub>
+<sub>Updated: 03/18/2023</sub>
 #### [`train-register-deploy-sklearn.ipynb`](train-register-deploy-sklearn.ipynb)
 
 
@@ -602,7 +602,7 @@ This notebook was developed on the conda pack with slug: `dataexpl_p37_cpu_v3`
 ---
 ### <a name="train-register-deploy-tensorflow.ipynb"></a> - Train, Register, and Deploy a TensorFlow Model
 
-<sub>Updated: 12/22/2022</sub>
+<sub>Updated: 03/18/2023</sub>
 #### [`train-register-deploy-tensorflow.ipynb`](train-register-deploy-tensorflow.ipynb)
 
 
@@ -632,34 +632,34 @@ This notebook was developed on the conda pack with slug: `nlp_p37_cpu_v2`
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="train-register-deploy-xgboost.ipynb"></a> - Train, Register, and Deploy an XGBoost Model
+### <a name="xgboost-with-rapids.ipynb"></a> - XGBoost with RAPIDS
 
 <sub>Updated: 12/22/2022</sub>
-#### [`train-register-deploy-xgboost.ipynb`](train-register-deploy-xgboost.ipynb)
+#### [`xgboost-with-rapids.ipynb`](xgboost-with-rapids.ipynb)
 
 
-Train, register, and deploy an XGBoost model.
+Compare training time between CPU and GPU trained models using XGBoost
 
-This notebook was developed on the conda pack with slug: `generalml_p38_cpu_v1`
+This notebook was developed on the conda pack with slug: `rapids2110_p37_gpu_v1`
 
 
-`xgboost`  `deploy model`  `register model`  `train model`
+`xgboost`  `rapids`  `gpu`  `machine learning`  `classification`
 
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="xgboost-with-rapids.ipynb"></a> - XGBoost with RAPIDS
+### <a name="train-register-deploy-xgboost.ipynb"></a> - Train, Register, and Deploy an XGBoost Model
 
-<sub>Updated: 12/22/2022</sub>
-#### [`xgboost-with-rapids.ipynb`](xgboost-with-rapids.ipynb)
+<sub>Updated: 03/18/2023</sub>
+#### [`train-register-deploy-xgboost.ipynb`](train-register-deploy-xgboost.ipynb)
 
 
-Compare training time between CPU and GPU trained models using XGBoost
+Train, register, and deploy an XGBoost model.
 
-This notebook was developed on the conda pack with slug: `rapids2110_p37_gpu_v1`
+This notebook was developed on the conda pack with slug: `generalml_p38_cpu_v1`
 
 
-`xgboost`  `rapids`  `gpu`  `machine learning`  `classification`
+`xgboost`  `deploy model`  `register model`  `train model`
 
 <sub>Universal Permissive License v 1.0</sup>
 

notebook_examples/accelerate-scikit_learn-with-intel_extension.ipynb

@@ -8,7 +8,7 @@
     "@notebook{accelerate-scikit_learn-with-intel_extension.ipynb,\n",
     "    title: Intel Extension for Scikit-Learn,\n",
     "    summary: Enhance performance of scikit-learn models using the Intel(R) oneAPI Data Analytics Library. Train a k-means model using both sklearn and the accelerated Intel library and compare performance.,\n",
-    "    developed on: sklearnex202130_p37_cpu_v1,\n",
+    "    developed_on: sklearnex202130_p37_cpu_v1,\n",
     "    keywords: intel, intel extension, scikit-learn, scikit learn,\n",
     "    license: Universal Permissive License v 1.0\n",
     "}"

notebook_examples/audi-autonomous_driving-oracle_open_data.ipynb

@@ -7,7 +7,7 @@
     "@notebook{audi-autonomous_driving-oracle_open_data.ipynb,\n",
     "    title: Audi Autonomous Driving Dataset Repository,\n",
     "    summary: Download, process and display autonomous driving data, and map LiDAR data onto images.,\n",
-    "    developed on: computervision_p37_cpu_v1,\n",
+    "    developed_on: computervision_p37_cpu_v1,\n",
     "    keywords: autonomous driving, oracle open data,\n",
     "    license: Universal Permissive License v 1.0\n",
     "}"

notebook_examples/automlx-anomaly_detection.ipynb

@@ -8,7 +8,7 @@
     "@notebook{automlx-anomaly_detection.ipynb,\n",
     "    title: Building and Explaining an Anomaly Detector using AutoMLx - Experimental\n",
     "    summary: Build an anomaly detection model using the experimental, fully unsupervised anomaly detection pipeline in Oracle AutoMLx for the public Credit Card Fraud dataset.,\n",
-    "    developed on: automlx_p38_cpu_v2,\n",
+    "    developed_on: automlx_p38_cpu_v2,\n",
     "    keywords: automlx, anomaly detection,\n",
     "    license: Universal Permissive License v 1.0,\n",
     "    original source: https://github.com/oracle-samples/automlx/blob/main/demos/OracleAutoMLx_AnomalyDetection.ipynb \n",

notebook_examples/automlx-classifier.ipynb

@@ -8,7 +8,7 @@
     "@notebook{automlx-classifier.ipynb,\n",
     "    title: Building and Explaining a Classifier using AutoMLx,\n",
     "    summary: Build a classifier using the Oracle AutoMLx tool and binary data set of Census income data.,\n",
-    "    developed on: automlx_p38_cpu_v2,\n",
+    "    developed_on: automlx_p38_cpu_v2,\n",
     "    keywords: automlx, classification, classifier,\n",
     "    license: Universal Permissive License v 1.0,\n",
     "    original source: https://github.com/oracle-samples/automlx/blob/main/demos/OracleAutoMLx_Classification.ipynb\n",

notebook_examples/automlx-forecasting.ipynb

@@ -8,7 +8,7 @@
     "@notebook{automlx-forecasting.ipynb,\n",
     "    title: Building a Forecaster using AutoMLx,\n",
     "    summary: Use Oracle AutoMLx to build a forecast model with real-world data sets.,\n",
-    "    developed on: automlx_p38_cpu_v2,\n",
+    "    developed_on: automlx_p38_cpu_v2,\n",
     "    keywords: language services, string manipulation, regex, regular expression, natural language processing, NLP, part-of-speech tagging, named entity recognition, sentiment analysis, custom plugins,\n",
     "    license: Universal Permissive License v 1.0,\n",
     "    original source: https://github.com/oracle-samples/automlx/blob/main/demos/OracleAutoMLx_Forecasting.ipynb\n",

notebook_examples/automlx-regression.ipynb

@@ -8,7 +8,7 @@
     "@notebook{automlx-regression.ipynb,\n",
     "    title: Building and Explaining a Regressor using AutoMLx,\n",
     "    summary: Build a regressor using Oracle AutoMLx and a pricing data set. Training options will be explored and the resulting AutoMLx models will be evaluated.,\n",
-    "    developed on: automlx_p38_cpu_v2,\n",
+    "    developed_on: automlx_p38_cpu_v2,\n",
     "    keywords: automlx, regression,\n",
     "    license: Universal Permissive License v 1.0,\n",
     "    original source: https://github.com/oracle-samples/automlx/blob/main/demos/OracleAutoMLx_Regression.ipynb\n",

notebook_examples/automlx-text_classification.ipynb

@@ -8,7 +8,7 @@
     "@notebook{automlx-text_classification.ipynb,\n",
     "    title: Building and Explaining a Text Classifier using AutoMLx,\n",
     "    summary: build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset,\n",
-    "    developed on: automlx_p38_cpu_v2,\n",
+    "    developed_on: automlx_p38_cpu_v2,\n",
     "    keywords: automlx, text classification, text classifier,\n",
     "    license: Universal Permissive License v 1.0.,\n",
     "    original source: https://github.com/oracle-samples/automlx/blob/main/demos/OracleAutoMLx_Classification_Text.ipynb\n",

notebook_examples/big_data_service-(BDS)-kerberos.ipynb

@@ -8,7 +8,7 @@
     "@notebook{big_data_service-(BDS)-kerberos.ipynb,\n",
     "    title: Connect to Oracle Big Data Service,\n",
     "    summary: Connect to Oracle Big Data services using Kerberos.,\n",
-    "    developed on: pyspark30_p37_cpu_v5,\n",
+    "    developed_on: pyspark30_p37_cpu_v5,\n",
     "    keywords: kerberos, big data service, bds,\n",
     "    license: Universal Permissive License v 1.0\n",
     "}"

notebook_examples/big_data_service-(BDS)-livy.ipynb

@@ -8,7 +8,7 @@
     "@notebook{big_data_service-(BDS)-livy.ipynb,\n",
     "    title: Using Livy on the Big Data Service,\n",
     "    summary: Work interactively with a BDS cluster using Livy and two different connection techniques, SparkMagic (for a notebook environment) and with REST.,\n",
-    "    developed on: pyspark30_p37_cpu_v5,\n",
+    "    developed_on: pyspark30_p37_cpu_v5,\n",
     "    keywords: bds, big data service, livy,\n",
     "    license: Universal Permissive License v 1.0\n",
     "}"