Merge branch 'master' of github.com:dato-code/tutorials

bharathvadla · Apr 14, 2016 · 330cad6 · 330cad6
2 parents bd1ead6 + fa3e41c
commit 330cad6
Show file tree

Hide file tree

Showing 31 changed files with 12,116 additions and 1,037 deletions.
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
@@ -1873,7 +1873,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.11"
   }
  },
  "nbformat": 4,

diff --git a/strata-sj-2016/README.md b/strata-sj-2016/README.md
@@ -0,0 +1,26 @@
+# Strata + Hadoop World, San Jose, 2016
+
+This directory contains demo notebooks used for **Practical Machine Learning**, an all-day tutorial 
+at [Strata + Hadoop World, San Jose, 2016](http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/47056).
+The course is designed to introduce machine learning via real applications like 
+- building a recommender
+- detecting anomalies
+- analyzing time series data
+- image analysis using deep learning
+- predicting customer churn
+- deploying machine learning 
+
+## Setup Instructions
+
+You can browse the notebooks using Github's notebook viewer, but please note that
+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)
+- [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)
diff --git a/...016/deep-learning/Deep Learning for Image Classification and Finding Similar Images.ipynb b/...016/deep-learning/Deep Learning for Image Classification and Finding Similar Images.ipynb
diff --git a/strata-sj-2016/deep-learning/Strata-SJ-2016-Deeplearning.pptx b/strata-sj-2016/deep-learning/Strata-SJ-2016-Deeplearning.pptx
diff --git a/strata-sj-2016/deep-learning/image_similarity.ipynb b/strata-sj-2016/deep-learning/image_similarity.ipynb
diff --git a/strata-sj-2016/deep-learning/images/AA1.png b/strata-sj-2016/deep-learning/images/AA1.png
diff --git a/strata-sj-2016/deep-learning/images/alexnet.png b/strata-sj-2016/deep-learning/images/alexnet.png
diff --git a/strata-sj-2016/deep-learning/images/cifar.png b/strata-sj-2016/deep-learning/images/cifar.png
diff --git a/strata-sj-2016/deep-learning/images/evaluate.png b/strata-sj-2016/deep-learning/images/evaluate.png
diff --git a/strata-sj-2016/deep-learning/images/extract_features.png b/strata-sj-2016/deep-learning/images/extract_features.png
diff --git a/strata-sj-2016/deep-learning/images/improve.png b/strata-sj-2016/deep-learning/images/improve.png
diff --git a/strata-sj-2016/deep-learning/images/linear.png b/strata-sj-2016/deep-learning/images/linear.png
diff --git a/strata-sj-2016/deep-learning/images/load.png b/strata-sj-2016/deep-learning/images/load.png
diff --git a/strata-sj-2016/deep-learning/images/quadratic.png b/strata-sj-2016/deep-learning/images/quadratic.png
diff --git a/strata-sj-2016/deep-learning/images/spiral.1-2.2-2-2-2-2-2.jpg b/strata-sj-2016/deep-learning/images/spiral.1-2.2-2-2-2-2-2.jpg
diff --git a/strata-sj-2016/deep-learning/images/train.png b/strata-sj-2016/deep-learning/images/train.png
diff --git a/strata-sj-2016/deep-learning/images/workflow1.png b/strata-sj-2016/deep-learning/images/workflow1.png
diff --git a/strata-sj-2016/deep-learning/images/workflow2.png b/strata-sj-2016/deep-learning/images/workflow2.png
diff --git a/strata-sj-2016/deep-learning/images/workflow3.png b/strata-sj-2016/deep-learning/images/workflow3.png
diff --git a/strata-sj-2016/deep-learning/images/workflow4.png b/strata-sj-2016/deep-learning/images/workflow4.png
diff --git a/strata-sj-2016/intro-ml/getting-started-with-sframes.ipynb b/strata-sj-2016/intro-ml/getting-started-with-sframes.ipynb
diff --git a/strata-sj-2016/intro-ml/sentiment_analysis.ipynb b/strata-sj-2016/intro-ml/sentiment_analysis.ipynb
diff --git a/strata-sj-2016/recommendation-systems/README.md b/strata-sj-2016/recommendation-systems/README.md
@@ -1,4 +1,4 @@
-# Strata + Hadoop World, New York City, 2015
+# Strata + Hadoop World, San Jose 2016
 
 This directory contains demo notebooks used for the "Introduction to Recommender Systems", the second session of **Machine Learning 101**, an all-day tutorial at [Strata + Hadoop World, New York City, 2015](http://strataconf.com/big-data-conference-ny-2015/public/schedule/detail/43217).
 
@@ -15,7 +15,7 @@ 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 and unzip the datasets 
 
 ## Handy references
 

diff --git a/strata-sj-2016/recommendation-systems/Recommender DeepDive - Part 1.ipynb b/strata-sj-2016/recommendation-systems/Recommender DeepDive - Part 1.ipynb
diff --git a/strata-sj-2016/recommendation-systems/Recommender DeepDive - Part 2.ipynb b/strata-sj-2016/recommendation-systems/Recommender DeepDive - Part 2.ipynb
diff --git a/strata-sj-2016/recommendation-systems/strata-sj-2016-recommendation-systems.key b/strata-sj-2016/recommendation-systems/strata-sj-2016-recommendation-systems.key