Difference between revisions of "Tutorial: TensorFlow References"
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| − | This is a very good approach to TensorFlow which uses the ''wine'' data from [http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/ UC Irvine Machine Learning repository]. It uses the red wine data (winequality-red.csv). It starts with a 30-minute math tutorial presented by jenhsin0@gmail.com, and it covers entropy, variable dependency, dimensionality reduction, graph representation of models, activation functions in neural nets, loss functions, and gradient descent. After a 10-minute pause (which is part of the video), the next speaker starts with a Jupyter notebook exploring various aspects of the wine-quality data. The notebook and associated data are available from this [https://github.com/PythonWorkshop/intro-to-tensorflow github repository]. | + | This is a very good approach to TensorFlow which uses the ''wine'' data from [http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/ UC Irvine Machine Learning repository]. It uses the red wine data (winequality-red.csv). It starts with a 30-minute math tutorial presented by jenhsin0@gmail.com, and it covers entropy, variable dependency, dimensionality reduction, graph representation of models, activation functions in neural nets, loss functions, and gradient descent. After a 10-minute pause (which is part of the video), the next speaker starts with a Jupyter notebook exploring various aspects of the wine-quality data. The notebook and associated data are available from this [https://github.com/PythonWorkshop/intro-to-tensorflow github repository]. |
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Revision as of 11:02, 6 August 2016
--D. Thiebaut (talk) 12:00, 6 August 2016 (EDT)
References & Tutorials
Intro to ML, Google video
This is a very good approach to TensorFlow which uses the wine data from UC Irvine Machine Learning repository. It uses the red wine data (winequality-red.csv). It starts with a 30-minute math tutorial presented by jenhsin0@gmail.com, and it covers entropy, variable dependency, dimensionality reduction, graph representation of models, activation functions in neural nets, loss functions, and gradient descent. After a 10-minute pause (which is part of the video), the next speaker starts with a Jupyter notebook exploring various aspects of the wine-quality data. The notebook and associated data are available from this github repository.
