Turi renaming

bharathvadla · Jul 12, 2016 · a7b9e3d · a7b9e3d
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diff --git a/README.md b/README.md
@@ -1,14 +1,14 @@
-# Dato Tutorials
+# Turi Tutorials
 
-This repository contains materials for demos, tutorials, and talks by Dato Inc.
+This repository contains materials for demos, tutorials, and talks by Turi.
 You can browse the notebooks using Github's own notebook viewer. Note that some
 images may not be rendered correctly. 
 
 If you'd like to run it, you may register for GraphLab Create
-(https://dato.com/download/), then follow instructions to install.
+(https://turi.com/download/), then follow instructions to install.
 
-- GraphLab Create User Guide: http://dato.com/learn/userguide
-- GraphLab Forum: http://forum.dato.com/categories/graphlab-create
+- GraphLab Create User Guide: http://turi.com/learn/userguide
+- GraphLab Forum: http://forum.turi.com/categories/graphlab-create
 
 # Events
 

diff --git a/dss-2016/README.md b/dss-2016/README.md
@@ -1,7 +1,7 @@
 # Strata + Hadoop World, San Jose, 2016
 
 This directory contains demo notebooks used for the collection of **machine
-learning tutorials** at the [Data Science Summit 2016](http://conf.dato.com/2016/us/). 
+learning tutorials** at the [Data Science Summit 2016](http://conf.turi.com/2016/us/). 
 
 The course is designed to introduce machine learning via real applications like 
 - recommender systems
@@ -17,5 +17,5 @@ and run the notebooks on your own machine.
 
 ## Handy references
 
-- [GraphLab Create User Guide](http://dato.com/learn/userguide)
-- [GraphLab Forum](http://forum.dato.com/categories/graphlab-create)
+- [GraphLab Create User Guide](http://turi.com/learn/userguide)
+- [GraphLab Forum](http://forum.turi.com/categories/graphlab-create)
diff --git a/dss-2016/churn_prediction/churn-tutorial.ipynb b/dss-2016/churn_prediction/churn-tutorial.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Forecasting customer churn with GraphLab Create\n",
     "\n",
-    "Churn prediction is the task of identifying users that are likely to stop using a service, product or website. Use Dato’s Churn Prediction Toolkit to accurately identify users that are at risk of churning. \n",
+    "Churn prediction is the task of identifying users that are likely to stop using a service, product or website. Use Turi’s Churn Prediction Toolkit to accurately identify users that are at risk of churning. \n",
     "\n",
     "In this tutorial, you will learn how to:\n",
     "\n",
@@ -50,14 +50,14 @@
      "text": [
       "A newer version of GraphLab Create (v1.10.1) is available! Your current version is v1.10.\n",
       "\n",
-      "You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.\n"
+      "You can use pip to upgrade the graphlab-create package. For more information see https://turi.com/products/create/upgrade.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "This commercial license of GraphLab Create is assigned to engr@dato.com.\n"
+      "This commercial license of GraphLab Create is assigned to engr@turi.com.\n"
      ]
     },
     {
@@ -1245,7 +1245,7 @@
     "a user/customer is said to have churned if periods of activity is followed\n",
     "by no activity for a `churn_period` (for example, 30 days). \n",
     "\n",
-    "<img src=\"https://dato.com/learn/userguide/churn_prediction/images/churn-illustration.png\", align=\"left\">"
+    "<img src=\"https://turi.com/learn/userguide/churn_prediction/images/churn-illustration.png\", align=\"left\">"
    ]
   },
   {

diff --git a/dss-2016/recommendation-systems/Recommender DeepDive - Part 1.ipynb b/dss-2016/recommendation-systems/Recommender DeepDive - Part 1.ipynb
diff --git a/dss-2016/recommendation-systems/Recommender DeepDive - Part 2.ipynb b/dss-2016/recommendation-systems/Recommender DeepDive - Part 2.ipynb
diff --git a/dss-2016/recommendation-systems/book-recommender-exercises.ipynb b/dss-2016/recommendation-systems/book-recommender-exercises.ipynb
@@ -29,7 +29,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO] This commercial license of GraphLab Create is assigned to engr@dato.com.\n",
+      "[INFO] This commercial license of GraphLab Create is assigned to engr@turi.com.\n",
       "\n",
       "[INFO] Start server at: ipc:///tmp/graphlab_server-41686 - Server binary: /Users/chris/miniconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1443482376.log\n",
       "[INFO] GraphLab Server Version: 1.6.1\n"
@@ -172,7 +172,7 @@
    "source": [
     "This dataset has multiple rows corresponding to the same book, e.g., in situations where reprintings were done by different publishers in different year.\n",
     "\n",
-    "For each unique value of 'book' in the `items` SFrame, select one of the of the available values for `author`, `publisher`, and `year`. Hint: Try using [`SFrame.groupby`](http://dato.com/products/create/docs/graphlab.data_structures.html#module-graphlab.aggregate) and [`gl.aggregate.SELECT_ONE`](http://dato.com/products/create/docs/graphlab.data_structures.html#graphlab.aggregate.SELECT_ONE)."
+    "For each unique value of 'book' in the `items` SFrame, select one of the of the available values for `author`, `publisher`, and `year`. Hint: Try using [`SFrame.groupby`](http://turi.com/products/create/docs/graphlab.data_structures.html#module-graphlab.aggregate) and [`gl.aggregate.SELECT_ONE`](http://turi.com/products/create/docs/graphlab.data_structures.html#graphlab.aggregate.SELECT_ONE)."
    ]
   },
   {

diff --git a/dss-2016/recommendation-systems/book-recommender-solutions.ipynb b/dss-2016/recommendation-systems/book-recommender-solutions.ipynb
@@ -38,7 +38,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "This commercial license of GraphLab Create is assigned to engr@dato.com.\n"
+      "This commercial license of GraphLab Create is assigned to engr@turi.com.\n"
      ]
     }
    ],
@@ -652,7 +652,7 @@
    "source": [
     "This dataset has multiple rows corresponding to the same book, e.g., in situations where reprintings were done by different publishers in different year.\n",
     "\n",
-    "For each unique value of 'book' in the `items` SFrame, select one of the of the available values for `author`, `publisher`, and `year`. Hint: Try using [`SFrame.groupby`](http://dato.com/products/create/docs/graphlab.data_structures.html#module-graphlab.aggregate) and [`gl.aggregate.SELECT_ONE`](http://dato.com/products/create/docs/graphlab.data_structures.html#graphlab.aggregate.SELECT_ONE)."
+    "For each unique value of 'book' in the `items` SFrame, select one of the of the available values for `author`, `publisher`, and `year`. Hint: Try using [`SFrame.groupby`](http://turi.com/products/create/docs/graphlab.data_structures.html#module-graphlab.aggregate) and [`gl.aggregate.SELECT_ONE`](http://turi.com/products/create/docs/graphlab.data_structures.html#graphlab.aggregate.SELECT_ONE)."
    ]
   },
   {

diff --git a/strata-nyc-2015/README.md b/strata-nyc-2015/README.md
@@ -11,10 +11,10 @@ Along the way, we also cover feature engineering and deploying machine learning
 
 You can browse the notebooks using Github iPython notebook viewer. Note that some images may not be rendered correctly If you'd like to run it, follow these steps to set up your machine.
 
-- [Download](https://dato.com/download/) GraphLab Create and then follow instructions to [install](https://dato.com/download/install.html).
-- Download and unzip the datasets [[831MB]](http://static.dato.com/ml101_datasets_stratanyc_2015.zip)
+- [Download](https://turi.com/download/) GraphLab Create and then follow instructions to [install](https://turi.com/download/install.html).
+- Download and unzip the datasets [[831MB]](http://static.turi.com/datasets/ml101_datasets_stratanyc_2015.zip)
 
 ## Handy references
 
-- [GraphLab Create User Guide](http://dato.com/learn/userguide)
-- [GraphLab Forum](http://forum.dato.com/categories/graphlab-create)
+- [GraphLab Create User Guide](http://turi.com/learn/userguide)
+- [GraphLab Forum](http://forum.turi.com/categories/graphlab-create)
diff --git a/...015/deep-learning/Deep Learning for Image Classification and Finding Similar Images.ipynb b/...015/deep-learning/Deep Learning for Image Classification and Finding Similar Images.ipynb
diff --git a/strata-nyc-2015/deep-learning/image_similarity.ipynb b/strata-nyc-2015/deep-learning/image_similarity.ipynb
@@ -82,7 +82,7 @@
        "output_type": "stream",
        "stream": "stderr",
        "text": [
-        "[INFO] This commercial license of GraphLab Create is assigned to engr@dato.com.\n",
+        "[INFO] This commercial license of GraphLab Create is assigned to engr@turi.com.\n",
         "\n"
        ]
       },

diff --git a/strata-nyc-2015/deployment/predictive_services.ipynb b/strata-nyc-2015/deployment/predictive_services.ipynb
diff --git a/strata-nyc-2015/deployment/scikit_deployment.ipynb b/strata-nyc-2015/deployment/scikit_deployment.ipynb
@@ -7,7 +7,7 @@
     "Introduction to ML Deployment\n",
     "==================\n",
     "\n",
-    "Deploying models created using python in a Dato Predictive Service is very easy. This notebook walks you through the step-by-step process. \n",
+    "Deploying models created using python in a Turi Predictive Service is very easy. This notebook walks you through the step-by-step process. \n",
     "\n",
     "<img src='images/predictive_services_overview.png'></img>\n",
     "\n",
@@ -74,7 +74,7 @@
    "source": [
     "We can expose the trained model as a REST endpoint. This will allow other applications to consume the predictions from the model.  \n",
     "\n",
-    "In order to do that, we wrap the model object in a Python function and add it to the Predictive Service. In the function you may add your own logic for transform input to the model, ensemble different models or manipulate output before returning. Checkout out [user guide](http://dato.com/learn/userguide/#Deployment) for more details.\n",
+    "In order to do that, we wrap the model object in a Python function and add it to the Predictive Service. In the function you may add your own logic for transform input to the model, ensemble different models or manipulate output before returning. Checkout out [user guide](http://turi.com/learn/userguide/#Deployment) for more details.\n",
     "\n",
     "The result of the function needs to be  a **JSON serializable** object."
    ]

diff --git a/strata-nyc-2015/feature_engineering/Feature Engineering for Text Data.ipynb b/strata-nyc-2015/feature_engineering/Feature Engineering for Text Data.ipynb
@@ -52,7 +52,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO] This commercial license of GraphLab Create is assigned to engr@dato.com.\n",
+      "[INFO] This commercial license of GraphLab Create is assigned to engr@turi.com.\n",
       "\n",
       "[INFO] Start server at: ipc:///tmp/graphlab_server-28139 - Server binary: /Users/alicez/.graphlab/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1443318283.log\n",
       "[INFO] GraphLab Server Version: 1.6.1\n"

diff --git a/strata-nyc-2015/feature_engineering/PCA demo.ipynb b/strata-nyc-2015/feature_engineering/PCA demo.ipynb
@@ -62,7 +62,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO] This commercial license of GraphLab Create is assigned to engr@dato.com.\n",
+      "[INFO] This commercial license of GraphLab Create is assigned to engr@turi.com.\n",
       "\n",
       "[INFO] Start server at: ipc:///tmp/graphlab_server-28311 - Server binary: /Users/alicez/.graphlab/anaconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1443318612.log\n",
       "[INFO] GraphLab Server Version: 1.6.1\n"

diff --git a/strata-nyc-2015/recommendation-systems/README.md b/strata-nyc-2015/recommendation-systems/README.md
@@ -14,10 +14,10 @@ Along the way, we also cover feature engineering and deploying machine learning
 
 You can browse the notebooks using Github IPython notebook viewer. Note that some images may not be rendered correctly. If you'd like to run it, follow these steps to set up your machine.
 
-- [Download](https://dato.com/download/) GraphLab Create and then follow instructions to [install](https://dato.com/download/install.html).
-- Download and unzip the datasets [[831MB]](http://static.dato.com/ml101_datasets_stratanyc_2015.zip)
+- [Download](https://turi.com/download/) GraphLab Create and then follow instructions to [install](https://turi.com/download/install.html).
+- Download and unzip the datasets [[831MB]](https://static.turi.com/datasets/ml101_datasets_stratanyc_2015.zip)
 
 ## Handy references
 
-- [GraphLab Create User Guide](http://dato.com/learn/userguide)
-- [GraphLab Forum](http://forum.dato.com/categories/graphlab-create)
+- [GraphLab Create User Guide](http://turi.com/learn/userguide)
+- [GraphLab Forum](http://forum.turi.com/categories/graphlab-create)
diff --git a/strata-nyc-2015/recommendation-systems/Recommender DeepDive - Part 1.ipynb b/strata-nyc-2015/recommendation-systems/Recommender DeepDive - Part 1.ipynb
diff --git a/strata-nyc-2015/recommendation-systems/Recommender DeepDive - Part 2.ipynb b/strata-nyc-2015/recommendation-systems/Recommender DeepDive - Part 2.ipynb
diff --git a/strata-nyc-2015/recommendation-systems/book-recommender-exercises.ipynb b/strata-nyc-2015/recommendation-systems/book-recommender-exercises.ipynb
@@ -29,7 +29,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[INFO] This commercial license of GraphLab Create is assigned to engr@dato.com.\n",
+      "[INFO] This commercial license of GraphLab Create is assigned to engr@turi.com.\n",
       "\n",
       "[INFO] Start server at: ipc:///tmp/graphlab_server-41686 - Server binary: /Users/chris/miniconda/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1443482376.log\n",
       "[INFO] GraphLab Server Version: 1.6.1\n"
@@ -172,7 +172,7 @@
    "source": [
     "This dataset has multiple rows corresponding to the same book, e.g., in situations where reprintings were done by different publishers in different year.\n",
     "\n",
-    "For each unique value of 'book' in the `items` SFrame, select one of the of the available values for `author`, `publisher`, and `year`. Hint: Try using [`SFrame.groupby`](http://dato.com/products/create/docs/graphlab.data_structures.html#module-graphlab.aggregate) and [`gl.aggregate.SELECT_ONE`](http://dato.com/products/create/docs/graphlab.data_structures.html#graphlab.aggregate.SELECT_ONE)."
+    "For each unique value of 'book' in the `items` SFrame, select one of the of the available values for `author`, `publisher`, and `year`. Hint: Try using [`SFrame.groupby`](http://turi.com/products/create/docs/graphlab.data_structures.html#module-graphlab.aggregate) and [`gl.aggregate.SELECT_ONE`](http://turi.com/products/create/docs/graphlab.data_structures.html#graphlab.aggregate.SELECT_ONE)."
    ]
   },
   {

diff --git a/strata-nyc-2015/recommendation-systems/book-recommender-solutions.ipynb b/strata-nyc-2015/recommendation-systems/book-recommender-solutions.ipynb
diff --git a/strata-sj-2016/README.md b/strata-sj-2016/README.md
@@ -16,11 +16,11 @@ You can browse the notebooks using Github's notebook viewer, but please note tha
 some images may not be rendered correctly. Follow these
 steps to set up and run the notebooks on your own machine.
 
-- [Download](https://dato.com/download/) GraphLab Create v1.8.5 and then follow instructions to [install](https://dato.com/download/install.html).
-- Download and unzip the datasets [[831MB]](http://static.dato.com/ml101_datasets_stratasj_2016.zip)
+- [Download](https://turi.com/download/) GraphLab Create v1.8.5 and then follow instructions to [install](https://turi.com/download/install.html).
+- Download and unzip the datasets [[831MB]](https://static.turi.com/datasets/ml101_datasets_stratasj_2016.zip)
 - [Install Jupyter notebook](http://jupyter.readthedocs.org/en/latest/install.html) (needed only if you install GraphLab Create via command line)
 
 ## Handy references
 
-- [GraphLab Create User Guide](http://dato.com/learn/userguide)
-- [GraphLab Forum](http://forum.dato.com/categories/graphlab-create)
+- [GraphLab Create User Guide](http://turi.com/learn/userguide)
+- [GraphLab Forum](http://forum.turi.com/categories/graphlab-create)