Difference between revisions of "Tutorial: TensorFlow References"
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| − | This is a very good approach to TensorFlow which uses the ''wine quality'' data-set 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 quality'' data-set 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. | ||
| + | <br /> | ||
| + | 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 18:06, 6 August 2016
--D. Thiebaut (talk) 12:00, 6 August 2016 (EDT)
Contents
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
- Start with this article from Scientific American: Unveiling the Hidden Layers of Deep Learning, by Amanda Montanez, editor at Scientific American. This short article references Springtime for AI, the rise of deep-learning, published by Scientific American (you'll need a subscription to access this article.)
- Play with the TensorFlow playground, created by Daniel Smilkov and Shan Carter.
Machine-Learning Recipes
A very good (though quick) introduction to various Machine Learning concepts, presented by Josh Gordon. 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 quality data-set 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.
