Difference between revisions of "Machine Learning Experiments"
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* [https://ml4a.github.io/ Machine Learning for Artists] by Gene Kogan and Francis Tseng | * [https://ml4a.github.io/ Machine Learning for Artists] by Gene Kogan and Francis Tseng | ||
* [https://www.udemy.com/machinelearning/ Machine Learning A-Z™: Hands-On Python & R In Data Science] | * [https://www.udemy.com/machinelearning/ Machine Learning A-Z™: Hands-On Python & R In Data Science] | ||
* [http://blog.otoro.net/2016/05/07/backprop-neat/ Neural Network Evolution Playground with Backprop NEAT] | |||
==Experiments/Tests== | ==Experiments/Tests== | ||
*Running spam data in RecurrentJS | *Running spam data in RecurrentJS | ||
[[File:Learningspam.png|none|400px|Running spam data in RecurrentJS]] | [[File:Learningspam.png|none|400px|Running spam data in RecurrentJS]] | ||
Revision as of 22:34, 6 July 2017
References
Examples with source code
- 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-appropriate by UCL Creative Hub
Projects
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 in RecurrentJS
