Changing incorrect Flow commands in comments

kyanga2 · kyanga2 · commit a75aa74f95c6 · 2015-11-06T17:28:05.000-08:00
diff --git a/tutorials/deeplearning/.ipynb_checkpoints/deeplearning-checkpoint.ipynb b/tutorials/deeplearning/.ipynb_checkpoints/deeplearning-checkpoint.ipynb
diff --git a/tutorials/deeplearning/README.md b/tutorials/deeplearning/README.md
@@ -181,7 +181,7 @@ m1 <- h2o.deeplearning(
 summary(m1)
 ```
 
-Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModels "dl_model_first"`, and pressing Ctrl-Enter.
+Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModel "dl_model_first"`, and pressing Ctrl-Enter.
 
 ### Variable Importances
 Variable importances for Neural Network models are notoriously difficult to compute, and there are many [pitfalls](ftp://ftp.sas.com/pub/neural/importance.html). H2O Deep Learning has implemented the method of [Gedeon](http://cs.anu.edu.au/~./Tom.Gedeon/pdfs/ContribDataMinv2.pdf), and returns relative variable importances in descending order of importance.
diff --git a/tutorials/deeplearning/deeplearning.R b/tutorials/deeplearning/deeplearning.R
@@ -155,7 +155,7 @@ m1 <- h2o.deeplearning(
 )
 summary(m1)
 #
-#Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModels "dl_model_first"`, and pressing Ctrl-Enter.
+#Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModel "dl_model_first"`, and pressing Ctrl-Enter.
 #
 #### Variable Importances
 #Variable importances for Neural Network models are notoriously difficult to compute, and there are many [pitfalls](ftp://ftp.sas.com/pub/neural/importance.html). H2O Deep Learning has implemented the method of [Gedeon](http://cs.anu.edu.au/~./Tom.Gedeon/pdfs/ContribDataMinv2.pdf), and returns relative variable importances in descending order of importance.
diff --git a/tutorials/deeplearning/deeplearning.Rmd b/tutorials/deeplearning/deeplearning.Rmd
@@ -181,7 +181,7 @@ m1 <- h2o.deeplearning(
 summary(m1)
 ```
 
-Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModels "dl_model_first"`, and pressing Ctrl-Enter.
+Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModel "dl_model_first"`, and pressing Ctrl-Enter.
 
 ### Variable Importances
 Variable importances for Neural Network models are notoriously difficult to compute, and there are many [pitfalls](ftp://ftp.sas.com/pub/neural/importance.html). H2O Deep Learning has implemented the method of [Gedeon](http://cs.anu.edu.au/~./Tom.Gedeon/pdfs/ContribDataMinv2.pdf), and returns relative variable importances in descending order of importance.
diff --git a/tutorials/deeplearning/deeplearning.ipynb b/tutorials/deeplearning/deeplearning.ipynb
diff --git a/tutorials/deeplearning/deeplearning.md b/tutorials/deeplearning/deeplearning.md
@@ -181,7 +181,7 @@ m1 <- h2o.deeplearning(
 summary(m1)
 ```
 
-Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModels "dl_model_first"`, and pressing Ctrl-Enter.
+Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModel "dl_model_first"`, and pressing Ctrl-Enter.
 
 ### Variable Importances
 Variable importances for Neural Network models are notoriously difficult to compute, and there are many [pitfalls](ftp://ftp.sas.com/pub/neural/importance.html). H2O Deep Learning has implemented the method of [Gedeon](http://cs.anu.edu.au/~./Tom.Gedeon/pdfs/ContribDataMinv2.pdf), and returns relative variable importances in descending order of importance.

Original file line number	Diff line number	Diff line change
`@@ -155,7 +155,7 @@ m1 <- h2o.deeplearning(`
`155`	`155`	`)`
`156`	`156`	`summary(m1)`
`157`	`157`	`#`
`158`		-#Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModels "dl_model_first"`, and pressing Ctrl-Enter.
	`158`	+#Inspect the model in [Flow](http://localhost:54321/) for more information about model building etc. by issuing a cell with the content `getModel "dl_model_first"`, and pressing Ctrl-Enter.
`159`	`159`	`#`
`160`	`160`	`#### Variable Importances`
`161`	`161`	`#Variable importances for Neural Network models are notoriously difficult to compute, and there are many [pitfalls](ftp://ftp.sas.com/pub/neural/importance.html). H2O Deep Learning has implemented the method of [Gedeon](http://cs.anu.edu.au/~./Tom.Gedeon/pdfs/ContribDataMinv2.pdf), and returns relative variable importances in descending order of importance.`