Difference between revisions of "Machine Learning Experiments"

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* [http://cs231n.stanford.edu/syllabus.html CS231n: Convolutional Neural Networks for Visual Recognition] by Standford
* [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
* [https://www.youtube.com/watch?v=D7ZL45xS39I&feature=youtu.be Machine Learning Magic for Your Javascript Application (Google I/O'19)] (2019)


==Teaching Machine Learning==
==Teaching Machine Learning==

Revision as of 08:05, 9 May 2019

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