Tutorial: TensorFlow References

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--D. Thiebaut (talk) 12:00, 6 August 2016 (EDT)


References & Tutorials


This page contains resources that will help you get up to speed with Machine Learning and TensorFlow. I have attempted to list the resources in a logical order, so that if you start from scratch, you will be presented by increasingly more sophisticated concepts as you progress. Note that all these tutorials assume a good understanding of Python an of how to install various packages for Python.

Understanding Deep Neural Nets

TensorFlowPlayGround.png










Machine-Learning Recipes


A very good (though quick) introduction to various Machine Learning concepts. It is highly recommended to code all the examples presented.

  • Machine Learning Recipe #1: Hello world!


  • Machine Learning Recipe #2: Decision Trees


  • Machine Learning Recipe #3: What makes a good feature?


  • Machine Learning Recipe #4: Pipeline


  • Machine Learning Recipe #5: Writing your own classifier















Intro to Machine Learning and TensorFlow, 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.