Difference between revisions of "Machine Learning Experiments"

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==Cultural matters with Machine Learning==
==Cultural matters with Machine Learning==
*Kate Crawford and Vladan Joler, “Anatomy of an AI System: The Amazon Echo As An Anatomical Map of Human Labor, Data and Planetary Resources,” AI Now Institute and Share Lab, (September 7, 2018) [https://anatomyof.ai https://anatomyof.ai]
*[https://www.youtube.com/watch?v=fMym_BKWQzk The Trouble with Bias] NIPS 2017 Keynote by Kate Crawford
*[https://www.youtube.com/watch?v=fMym_BKWQzk The Trouble with Bias] NIPS 2017 Keynote by Kate Crawford
*[https://mitpress.mit.edu/books/artificial-unintelligence Artificial Unintelligence: How Computers Misunderstand the World] by Meredith Broussard
*[https://mitpress.mit.edu/books/artificial-unintelligence Artificial Unintelligence: How Computers Misunderstand the World] by Meredith Broussard

Revision as of 23:22, 9 December 2018

Introduction to Machine Learning

Cultural matters with Machine Learning

Technical explanation on Machine Learning

RNN/LSTM focus

Neural Network

Examples with source code

Mixed media (text/image)

Text related

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

  • 12/2018: Try running LSTM/tensorflow training on chinese text with python (again following text predictor)
weiboscope
  • 11/2018: Try running LSTM/tensorflow training with Python (following text predictor)from my PhD thesis text
PhD thesis
  • 11/2018: Try running local ml5 + python training with English text (multiple manifestos) and generate 10000 characters text from multiple manifestos
manifestos
  • 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