Difference between revisions of "Tutorial: TensorFlow References"
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| + | ==Understanding Deep Neural Nets== | ||
| + | [[Image:TensorFlowPlayGround.png|500px|right]] | ||
| + | * Start with this [http://blogs.scientificamerican.com/sa-visual/unveiling-the-hidden-layers-of-deep-learning/ article from Scientific American]: [http://blogs.scientificamerican.com/sa-visual/unveiling-the-hidden-layers-of-deep-learning/ Unveiling the Hidden Layers of Deep Learning, by Amanda Montanez, editor at Scientific American]. | ||
| + | * Play with the [http://playground.tensorflow.org/#activation=relu&batchSize=21&dataset=spiral®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=10&networkShape=3,3,2&seed=0.56096&showTestData=true&discretize=true&percTrainData=70&x=false&y=true&xTimesY=true&xSquared=true&ySquared=false&cosX=false&sinX=true&cosY=false&sinY=true&collectStats=false&problem=classification&initZero=false TensorFlow playground], created by Daniel Smilkov and Shan Carter. | ||
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==Intro to Machine Learning and TensorFlow, Google video== | ==Intro to Machine Learning and TensorFlow, Google video== | ||
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Revision as of 11:24, 6 August 2016
--D. Thiebaut (talk) 12:00, 6 August 2016 (EDT)
References & Tutorials
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.
- Play with the TensorFlow playground, created by Daniel Smilkov and Shan Carter.
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.
