Difference between revisions of "Machine Learning Experiments"

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* [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]
* [https://www.youtube.com/watch?v=nAHcrz5hxc4 ConvNetJS: Deep Learning in the Browser] [Video] by Christoph Körner (more technical)
* [https://www.youtube.com/watch?v=nAHcrz5hxc4 ConvNetJS: Deep Learning in the Browser] [Video] by Christoph Körner (more technical)
* [http://cs231n.stanford.edu/syllabus.html CS231n: Convolutional Neural Networks for Visual Recognition] by Standford
* [http://sharedli.st/jhave2 Machine Learning resource list] by David Jhave Johnston
* [http://sharedli.st/jhave2 Machine Learning resource list] by David Jhave Johnston



Revision as of 17:37, 29 August 2017

Introduction to Machine Learning

Cultural matters with Machine Learning

Technical explanation on Machine Learning

RNN focus

Neural Network

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." + his interview on why javascript and machine learning.
  • Re-appropriation of Recurrentjs by UCL Creative Hub

Image related

Projects

Text related

Artworks

Exhibition

Learning resource

Teaching Machine Learning

Experiments/Tests

  • Running spam data with RecurrentJS in a local browser (Winnie)
Running spam data with RecurrentJS
  • Running a jpg file with ConvNetJS in a local browser (Winnie)
Learning a jpg file with ConvNetJS
  • Running a PNG file with Synaptic.js in a local browser (Winnie)
Learning a png file with Synaptic
  • Running a customized neural network in a local browser (Winnie)
Learning XOR with Synaptic