Difference between revisions of "Machine Learning Experiments"
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==References== | ==References== | ||
*[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 | ||
*[http://www.aprja.net/entanglement-machine-learning-and-human-ethics-in-driver-less-car-crashes/ Entanglement: Machine learning and human ethics in driver-less car crashes] by Maya Indira Ganesh | |||
==Examples with source code== | ==Examples with source code== | ||
Revision as of 18:23, 7 July 2017
References
- A Return to Machine Learning [Video] by Kyle McDonald
- 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 Kaparthy, 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
Projects
[Text related]
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
- Recurrent Neural Network Tutorial for Artists - with handwriting generation demo in p5.js
Experiments/Tests
- Running spam data in RecurrentJS on a local browser (Winnie)
