Difference between revisions of "Machine Learning Experiments"

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*[https://quamproxime.com/2017/07/30/the-temporality-of-artificial-intelligence/ The Temporality of Artificial Intelligence (2017)] by Kathryn Hume
*[https://quamproxime.com/2017/07/30/the-temporality-of-artificial-intelligence/ The Temporality of Artificial Intelligence (2017)] by Kathryn Hume


===Technical explaination with Machine Learning==
==Technical explanation with Machine Learning==
*[http://karpathy.github.io/2015/05/21/rnn-effectiveness/ The Unreasonable Effectiveness of Recurrent Neural Networks] (2015) by Andrej Karpathy
*[http://karpathy.github.io/2015/05/21/rnn-effectiveness/ The Unreasonable Effectiveness of Recurrent Neural Networks] (2015) by Andrej Karpathy
 
==Examples with source code==
==Examples with source code==
[Text related]
[Text related]

Revision as of 13:51, 9 August 2017

Introduction to Machine Learning

Cultural matters with Machine Learning

Technical explanation with Machine Learning

Examples with source code

[Text related]

  • Chinese receipt OCR using Tensorflow | SpikeFlow (Blog | Github)
  • Recurrentjs by Andrej Karpathy, mainly for text training. "Sentences are input data and the networks are trained to predict the next character in a sentence."
  • Re-appropriation of Recurrentjs by UCL Creative Hub

[Image related]

Projects

[Text related]

[Artworks]

Learning resource

Experiments/Tests

  • Running spam data with RecurrentJS in a local browser (Winnie)
Running spam data with RecurrentJS
  • Running a jpg file with ConvNetJS in a local browser (Winnie)
Learning a jpg file with ConvNetJS
  • Running a PNG file with Synaptic.js in a local browser (Winnie)
Learning a png file with Synaptic