Difference between revisions of "Tutorial: Playing with the Boston Housing Data"
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| − | =Deep Neural | + | =Deep Neural-Network Regressor (DNNRegressor from Tensorflow)= |
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| − | This tutorial follows very closely two other good tutorials and merges elements from both: | + | This tutorial uses '''SKFlow''' and follows very closely two other good tutorials and merges elements from both: |
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* https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/skflow/boston.py | * https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/skflow/boston.py | ||
Revision as of 16:18, 8 August 2016
--D. Thiebaut (talk) 16:06, 8 August 2016 (EDT)
Deep Neural-Network Regressor (DNNRegressor from Tensorflow)
This tutorial uses SKFlow and follows very closely two other good tutorials and merges elements from both:
- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/skflow/boston.py
- http://bigdataexaminer.com/uncategorized/how-to-run-linear-regression-in-python-scikit-learn/
Both use the Boston housing prices data.
The tutorial is best viewed as a Jupyter notebook (available in zipped form below), or as a static pdf (you'll have to retype all the commands...)
- Jupyter Notebook (Unzip before using)
