Difference between revisions of "Machine Learning Experiments"
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==Experiments/Tests== | ==Experiments/Tests== | ||
*Running spam data with RecurrentJS | *Running spam data with RecurrentJS on a local browser (Winnie) | ||
[[File:Learningspam.png|none|400px|Running spam data with RecurrentJS]] | [[File:Learningspam.png|none|400px|Running spam data with RecurrentJS]] | ||
*Running a jpg file with ConvNetJS | *Running a jpg file with ConvNetJS on a local browser (Winnie) | ||
[[File:ML_JS_64.png|none|400px|Learning a jpg file with ConvNetJS]] | [[File:ML_JS_64.png|none|400px|Learning a jpg file with ConvNetJS]] | ||
*Running a PNG file with Synaptic.js in a local browser (Winnie) | *Running a PNG file with Synaptic.js in a local browser (Winnie) | ||
| Line 80: | Line 80: | ||
*Running a customized neural network in a local browser (Winnie) | *Running a customized neural network in a local browser (Winnie) | ||
[[File:Neuralnetwork.png|none|400px|Learning XOR with Synaptic]] | [[File:Neuralnetwork.png|none|400px|Learning XOR with Synaptic]] | ||
*Running ml5.js example - Simple Image Classification Example | *Running ml5.js example - Simple Image Classification Example on a local browser (Winnie) | ||
[[File:Ml5.png|none|400px|Predicting what's the image with confidence level]] | [[File:Ml5.png|none|400px|Predicting what's the image with confidence level]] | ||
Revision as of 06:55, 30 March 2018
Introduction to Machine Learning
- A Return to Machine Learning [Video] by Kyle McDonald
- The 7 steps of Machine Learning (2017) [Video] by Yufeng/Google Cloud
- A visual introduction to machine learning by r2d3.
- Recurrent Neural Network Tutorial for Artists - with handwriting generation demo in p5.js
Cultural matters with Machine Learning
- Entanglement: Machine learning and human ethics in driver-less car crashes (2017) by Maya Indira Ganesh
- Biased bots: Human prejudices sneak into AI systems by Joanna Bryson
- The Temporality of Artificial Intelligence (2017) by Kathryn Hume
- Mackenzie Adrian, The production of prediction: What does machine learning want?,European Journal of Cultural Studies2015, Vol.18(4-5) 429–445
- In the data: interdisciplinary modes of machine learning by Adrian Mackenzie
- Automating Aesthetics: Artificial Intelligence and Image Culture (2017) by Lev Manovich
- Ed Finn, "Building the Star Trek Computer," in What Algorithms Want, MIT Press, 2017, pp. 57-85.
- Mackenzie Adrian, Machine Learners:Archaeology of a Data Practice, MIT Press, 2017
- The Author Function: Imitating Grant Allen with Queer Writing Machines(2017) by Tiffany Chan (with source code)
- Cox, Geoff. Ways of machine seeing. Unthinking Photography, 2016.
Technical explanation on Machine Learning
RNN focus
- The Unreasonable Effectiveness of Recurrent Neural Networks (2015) by Andrej Karpathy
Neural Network
- How to create a Neural Network in JavaScript in only 30 lines of code (2017) by Per Harald Borgen, see sceencast here. Source code here
Examples with source code
Mixed media (text/image)
- ML5.js library developed by NYU ITP
- 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 paint: 1/ ConvNetJS Library by Andrej Karpathy 2/ Synaptic.js by Synaptic
- Deep roll by Tero Parviainen | source code built with TensorFlow and js
Projects
- THE TEXT-GENERATING RNN DEMO by Ilya Sutskever
- Handwriting Generation Demo by Alex Graves
- Obama RNN by Samim
- RNN Bible on Twitter
Artworks
- Critter Compiler prototype by Helen Pritchard (2016)
- Cloud index by James Bridle | Read his interview here on James Bridle: Machine Learning in Practice
- Big Data Poetry by David Jhave Johnston
Exhibition
- Artists & Robots (2017) at the Astana Contemporary Art Center in Kazakhstan
- Artificial Intelligence: The Other I (2017) Ars Electronica
Workshop
- Counting to 4: 0, 1, 2, 3 – Data Collection as Art Practice & Protest (2017) by Caroline Sinders
Demo/Experimental Projects
- Teachable Machine, example Rock out by wiggling your fingers, source code here
- Giorgio Cam by Eric Rosenbaum and Yotam Mann, source code here
Learning resource
- Daniel Shiffman's Nature of Code: Neural networks| video
- Machine Learning for Artists by Gene Kogan and Francis Tseng
- Machine Learning for Muscians and Artists by Rebecca Fiebrink (main), Laetitia Sonami (guest) and Baptiste Caramiaux (guest)
- Machine Learning, taught by Andrew Ng
- Machine Learning A-Z™: Hands-On Python & R In Data Science
- Neural Network Evolution Playground with Backprop NEAT
- ConvNetJS: Deep Learning in the Browser [Video] by Christoph Körner (more technical)
- CS231n: Convolutional Neural Networks for Visual Recognition by Standford
- Machine Learning resource list by David Jhave Johnston
Teaching Machine Learning
Experiments/Tests
- Running spam data with RecurrentJS on a local browser (Winnie)
- Running a jpg file with ConvNetJS on a local browser (Winnie)
- Running a PNG file with Synaptic.js in a local browser (Winnie)
- Running a customized neural network in a local browser (Winnie)
- Running ml5.js example - Simple Image Classification Example on a local browser (Winnie)
