Difference between revisions of "Machine Learning Experiments"
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==Experiments/Tests== | ==Experiments/Tests== | ||
*Running spam data with RecurrentJS | *Running spam data with RecurrentJS in a local browser (Winnie) | ||
[[File:Learningspam.png|none|400px|Running spam data with RecurrentJS]] | [[File:Learningspam.png|none|400px|Running spam data with RecurrentJS]] | ||
*Running a jpg file with ConvNetJS | *Running a jpg file with ConvNetJS in a local browser (Winnie) | ||
[[File:ML_JS_64.png|none|400px|Learning a jpg file with ConvNetJS]] | [[File:ML_JS_64.png|none|400px|Learning a jpg file with ConvNetJS]] | ||
Revision as of 17:11, 20 July 2017
Introduction to Machine Learning
- A Return to Machine Learning [Video] by Kyle McDonald
- ConvNetJS: Deep Learning in the Browser [Video] by Christoph Körner
- A visual introduction to machine learning by r2d3.
- Recurrent Neural Network Tutorial for Artists - with handwriting generation demo in p5.js
References
- Entanglement: Machine learning and human ethics in driver-less car crashes by Maya Indira Ganesh
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]
- ConvNetJS Library by Andrej Karpathy
Projects
[Text related]
[Artworks]
- Cloud index by James Bridle | Read his interview here on James Bridle: Machine Learning in Practice
Learning resource
- Daniel Shiffman's Nature of Code: Neural networks| video
- Machine Learning for Artists by Gene Kogan and Francis Tseng
- Machine Learning A-Z™: Hands-On Python & R In Data Science
- Neural Network Evolution Playground with Backprop NEAT
Experiments/Tests
- Running spam data with RecurrentJS in a local browser (Winnie)
- Running a jpg file with ConvNetJS in a local browser (Winnie)
