Difference between revisions of "Machine Learning Experiments"

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*[https://www.cityu.edu.hk/iscma/ Art Machines: International Symposium on Computational Media Art] (2019) organized by School of Creative Media, City University of Hong Kong
*[https://www.cityu.edu.hk/iscma/ Art Machines: International Symposium on Computational Media Art] (2019) organized by School of Creative Media, City University of Hong Kong
*[https://zkm.de/en/event/2018/04/encoding-cultures-living-amongst-intelligent-machines international conference »Encoding Cultures. Living Amongst Intelligent Machines«](2018)
*[https://zkm.de/en/event/2018/04/encoding-cultures-living-amongst-intelligent-machines international conference »Encoding Cultures. Living Amongst Intelligent Machines«](2018)
* [https://aoir.org/aoir2018/preconfwrkshop/#CL The Cultural Life of Machine Learning: An Incursion into Critical AI Studies
Preconference Workshop] (2018)


===Demo/Experimental Projects===
===Demo/Experimental Projects===

Revision as of 17:18, 27 June 2018

Introduction to Machine Learning

Cultural matters with Machine Learning

Technical explanation on Machine Learning

RNN focus

Neural Network

Examples with source code

Mixed media (text/image)

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

Sound related

Projects

Text related

Artworks

Examples/Performance/Speculative design

Exhibition

Workshop

Conference

Preconference Workshop] (2018)

Demo/Experimental Projects

Learning resource

Teaching Machine Learning

Experiments/Tests

  • 06/2018: Try running LSTM ml5js with training simplied chinese data. Source from weiboscope 2012 week 1 deleted text
weiboscope text
  • 06/2018: Running LSTM ml5js example with my own training data
Training Process
Outcome Process
  • 2018: Running ml5.js example - Simple LSTM Generator Example on a local browser
Predicting what's the text
  • Running ml5.js example - Simple Image Classification Example on a local browser
Predicting what's the image with confidence level
  • Running spam data with RecurrentJS on a local browser
Running spam data with RecurrentJS
  • Running a customized neural network on a local browser
Learning XOR with Synaptic
  • Running a PNG file with Synaptic.js on a local browser
Learning a png file with Synaptic
  • Running a jpg file with ConvNetJS on a local browser
Learning a jpg file with ConvNetJS