Google Facets - Revision history

BPeat: Text replacement - "http:" to "https:"

2023-03-28T20:28:40Z

Text replacement - "http:" to "https:"

BPeat at 23:17, 21 September 2020

2020-09-21T23:17:42Z

BPeat at 19:51, 21 September 2020

2020-09-21T19:51:48Z

BPeat at 00:36, 20 September 2020

2020-09-20T00:36:04Z

BPeat at 00:34, 20 September 2020

2020-09-20T00:34:54Z

BPeat at 05:06, 19 September 2020

2020-09-19T05:06:30Z

BPeat at 16:46, 22 August 2020

2020-08-22T16:46:39Z

BPeat at 16:45, 22 August 2020

2020-08-22T16:45:47Z

BPeat at 16:45, 22 August 2020

2020-08-22T16:45:31Z

BPeat at 16:43, 22 August 2020

2020-08-22T16:43:52Z

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=training+datasets YouTube search...]
+[https://www.youtube.com/results?search_query=training+datasets YouTube search...]
-[http://www.google.com/search?q=datasets+training+deep+machine+learning+artificial+intelligence+ML+AI ...Google search]
+[https://www.google.com/search?q=datasets+training+deep+machine+learning+artificial+intelligence+ML+AI ...Google search]
-* [http://pair-code.github.io/facets/ Facets] | [[Google]]
+* [https://pair-code.github.io/facets/ Facets] | [[Google]]
-** [http://goo.gle/38ZUlTD Hands-on labs]
+** [https://goo.gle/38ZUlTD Hands-on labs]
-** [http://github.com/PAIR-code/facets Facets - GitHub]
+** [https://github.com/PAIR-code/facets Facets - GitHub]
 * [[Data Science]]
 ** [[Data Governance]]
@@ Line 17: / Line 17: @@
 **** [[Feature Exploration/Learning]]
 **** [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
-* [http://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc Visualize your data with Facets | Yufeng G - Towards Data Science]
+* [https://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc Visualize your data with Facets | Yufeng G - Towards Data Science]
 Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets.
@@ Line 26: / Line 26: @@
 Overview takes input feature data from any number of datasets, analyzes them feature by feature and visualizes the analysis. Overview gives users a quick understanding of the distribution of values across the features of their dataset(s). Uncover several uncommon and common issues such as unexpected feature values, missing feature values for a large number of observation, training/serving skew and train/test/validation set skew.
-<img src="http://miro.medium.com/max/1000/1*FoCXpeJA5kZr1bBVFmQCrg.png" width="600">
+<img src="https://miro.medium.com/max/1000/1*FoCXpeJA5kZr1bBVFmQCrg.png" width="600">
@@ Line 32: / Line 32: @@
 Dive is a tool for interactively exploring large numbers of data points at once. Dive provides an interactive interface for exploring the relationship between data points across all of the different features of a dataset. Each individual item in the visualization represents a data point. Position items by "faceting" or bucketing them in multiple dimensions by their feature values. Success stories of Dive include the detection of classifier failure, identification of systematic errors, evaluating ground truth and potential new signals for ranking.
-<img src="http://miro.medium.com/max/1000/1*bGQMgbCspRFqfcMJM63D_g.gif" width="600">
+<img src="https://miro.medium.com/max/1000/1*bGQMgbCspRFqfcMJM63D_g.gif" width="600">
 <youtube>WVclIFyCCOo</youtube>
 <youtube>y7cem6PsDjg</youtube>

@@ Line 15: / Line 15: @@
 *** [[Benchmarks]]
 *** [[Data Preprocessing]]
-**** [[Feature Exploration/Learning]] ...inspection, data profiling, selection
+**** [[Feature Exploration/Learning]]
 **** [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 * [http://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc Visualize your data with Facets | Yufeng G - Towards Data Science]

@@ Line 16: / Line 16: @@
 *** [[Data Preprocessing]]
 **** [[Feature Exploration/Learning]] ...inspection, data profiling, selection
-**** [[Data Quality]] ...[[AI Verification and Validation|validity]], accuracy, [[Data Quality#Data Cleaning|cleaning]], completeness, consistency, uniformity, [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]],   [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
+**** [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 * [http://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc Visualize your data with Facets | Yufeng G - Towards Data Science]

@@ Line 17: / Line 17: @@
 **** [[Feature Exploration/Learning]] ...inspection, data profiling, selection
 **** [[Data Quality]] ...[[AI Verification and Validation|validity]], accuracy, [[Data Quality#Data Cleaning|cleaning]], completeness, consistency, uniformity, [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]],   [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 * [http://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc Visualize your data with Facets | Yufeng G - Towards Data Science]

@@ Line 11: / Line 11: @@
 ** [http://goo.gle/38ZUlTD Hands-on labs]
 ** [http://github.com/PAIR-code/facets Facets - GitHub]
-* [[Datasets]]
+* [[Data Science]]
-* [[Benchmarks]]
+** [[Data Governance]]
-* [[Batch Norm(alization) & Standardization]]
+*** [[Benchmarks]]
-* [[Data Preprocessing]]
+*** [[Data Preprocessing]]
-* [[Feature Exploration/Learning]]
+**** [[Feature Exploration/Learning]] ...inspection, data profiling, selection
 * [[Hyperparameter]]s
 * [http://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc Visualize your data with Facets | Yufeng G - Towards Data Science]

@@ Line 17: / Line 17: @@
 * [[Feature Exploration/Learning]]
 * [[Hyperparameter]]s
-* [[Data Augmentation]], Data Labeling, and Auto-Tagging
+* [[Data Augmentation, Data Labeling, and Auto-Tagging]]
 * [[Visualization]]
 * [[Master Data Management  (MDM) / Feature Store / Data Lineage / Data Catalog]]

@@ Line 23: / Line 23: @@
 Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets.
-*<b>Facets Overview</b> - get a sense of the shape of each feature of your dataset
+* <b>Facets Overview</b> - get a sense of the shape of each feature of your dataset
- *<b>Facets Dive</b> - explore individual observations
+* <b>Facets Dive</b> - explore individual observations
 = Facets Overview =