Difference between revisions of "Tutorial: TensorFlow References"
(→Understanding Deep Neural Nets) |
(→Understanding Deep Neural Nets) |
||
| Line 6: | Line 6: | ||
[[Image:TensorFlowPlayGround.png|500px|right]] | [[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]. This short article references [http://www.scientificamerican.com/article/springtime-for-ai-the-rise-of-deep-learning/ Springtime for AI, the rise of deep-learning], published by Scientific American (you'll need a subscription to access this article.) | * 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]. This short article references [http://www.scientificamerican.com/article/springtime-for-ai-the-rise-of-deep-learning/ Springtime for AI, the rise of deep-learning], published by Scientific American (you'll need a subscription to access this article.) | ||
| − | * 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. | + | <br /> |
| + | * '''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. | ||
<br /> | <br /> | ||
<br /> | <br /> | ||
Revision as of 11:29, 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. 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.
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.
