Difference between revisions of "Machine Learning Experiments"
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*Running ml5.js example - Simple LSTM Generator Example on a local browser (Winnie) | *Running ml5.js example - Simple LSTM Generator Example on a local browser (Winnie) | ||
[[File:LSTMText.png|none|400px|Predicting what's the text]] | [[File:LSTMText.png|none|400px|Predicting what's the text]] | ||
*Running LSTM ml5js example with my own training data | |||
[[File:text_training.png|none|200px|Training Process]] | |||
[[File:trained_text.png|none|200px|Outcome Process]] | |||
Revision as of 21:10, 25 June 2018
Introduction to Machine Learning
- Machine Learning for Creative Media [Video] 10 series workshop by Gene Kogan
- 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
- Deep Learning: A Critical Appraisal by Gary Marcus
Cultural matters with Machine Learning
- Entanglement: Machine learning and human ethics in driver-less car crashes (2017) by Maya Indira Ganesh
- //Machine Learning and the Complexities of Human Emotions// by Caroline Sinders
- 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, text training tutorial here
- 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
- Learning to see: Hello, World! (2017) by Memo Akten. More here
Examples/Performance/Speculative design
- Examples of Wekinator
- AYA pushes back by Marie Louise Juul Søndergaard
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
Conference
- Art Machines: International Symposium on Computational Media Art (2019) organized by School of Creative Media, City University of Hong Kong
- international conference »Encoding Cultures. Living Amongst Intelligent Machines«(2018)
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
- Webcam Pacman by BANDAI NAMCO Entertainment Inc.
- P5js KNN demos by Andreas Refsgaard
Learning resource
- ml5: Friendly Open Source Machine Learning Library for the Web by Daniel Shiffman and ml5.js collaborators
- 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 on a local browser (Winnie)
- Running a customized neural network on a local browser (Winnie)
- Running ml5.js example - Simple Image Classification Example on a local browser (Winnie)
- Running ml5.js example - Simple LSTM Generator Example on a local browser (Winnie)
- Running LSTM ml5js example with my own training data
