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 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
- A Return to Machine Learning [Video] by Kyle McDonald
- A visual introduction to machine learning by r2d3.
- Recurrent Neural Network Tutorial for Artists - with handwriting generation demo in p5.js
Cultural matters with Machine Learning
- Entanglement: Machine learning and human ethics in driver-less car crashes (2017) by Maya Indira Ganesh
- Biased bots: Human prejudices sneak into AI systems by Joanna Bryson
- The Temporality of Artificial Intelligence (2017) by Kathryn Hume
Technical explanation with Machine Learning
- The Unreasonable Effectiveness of Recurrent Neural Networks (2015) by Andrej Karpathy
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]
- Image paint: 1/ ConvNetJS Library by Andrej Karpathy 2/ Synaptic.js by Synaptic
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
- ConvNetJS: Deep Learning in the Browser [Video] by Christoph Körner (more technical)
Experiments/Tests
- Running spam data with RecurrentJS in a local browser (Winnie)
- Running a jpg file with ConvNetJS in a local browser (Winnie)
- Running a PNG file with Synaptic.js in a local browser (Winnie)