Difference between revisions of "Machine Learning Experiments"

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==Introduction to Machine Learning==
==Introduction to Machine Learning==
*[https://www.youtube.com/watch?v=POrPIABj2MI A Return to Machine Learning] [Video] by Kyle McDonald
*[https://www.youtube.com/watch?v=POrPIABj2MI A Return to Machine Learning] [Video] by Kyle McDonald
*[https://www.youtube.com/watch?v=nAHcrz5hxc4 ConvNetJS: Deep Learning in the Browser] [Video] by Christoph Körner (more technical)
*[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ A visual introduction to machine learning] by r2d3.
*[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ A visual introduction to machine learning] by r2d3.
* [http://blog.otoro.net/2017/01/01/recurrent-neural-network-artist/ Recurrent Neural Network Tutorial for Artists - with handwriting generation demo in p5.js]
* [http://blog.otoro.net/2017/01/01/recurrent-neural-network-artist/ Recurrent Neural Network Tutorial for Artists - with handwriting generation demo in p5.js]

Revision as of 17:49, 20 July 2017

Introduction to Machine Learning

References

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