Difference between revisions of "Machine Learning Experiments"
Jump to navigation
Jump to search
| Line 24: | Line 24: | ||
* [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] | * [http://blog.otoro.net/2016/05/07/backprop-neat/ Neural Network Evolution Playground with Backprop NEAT] | ||
==Experiments/Tests== | ==Experiments/Tests== | ||
*Running spam data with RecurrentJS on a local browser (Winnie) | *Running spam data with RecurrentJS on a local browser (Winnie) | ||
[[File:Learningspam.png|none|400px|Running spam data in RecurrentJS]] | [[File:Learningspam.png|none|400px|Running spam data in RecurrentJS]] | ||
Revision as of 19:45, 17 July 2017
Introduction to Machine Learning
- A Return to Machine Learning [Video] by Kyle McDonald
- A visual introduction to machine learning by r2d3.
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 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]
[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 on a local browser (Winnie)
