Machine Learning Experiments
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
- Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard
- Design Justice, A.I., and Escape from the Matrix of Domination (2018) by Sasha Costanza-Chock
- 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/LSTM focus
- Understanding LSTM Networks (2015) by Christopher Colah
- 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
- text predictor by Greg Surma (python 2.7/3.+ with tensorflow (RNN+LSTM)
- Semantic similarity chatbot (with movie dialog) by Allison Parrish
- 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
- Notes on remixing Noon, generative text and Markov chains by Rev Dan Catt
Artworks
- of the soone by Sofian Audry (2017)
- 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
- The Work of Art in the Age of Artificial Intelligence Workshop (2018) by media art curator Natalia Fuchs and media artist Helena Nikonole
- 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)
- [https://aoir.org/aoir2018/preconfwrkshop/#CL The Cultural Life of Machine Learning: An Incursion into Critical AI Studies
Preconference Workshop] (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
- 11/2018: Try running LSTM/tensorflow training with Python (following text predictor)from my PhD thesis text
- 11/2018: Try running local ml5 + python training with English text (multiple manifestos) and generate 10000 characters text from multiple manifestos
- 06/2018: Try running LSTM ml5js with training simplied chinese data. Source from weiboscope 2012 week 1 deleted text
- 06/2018: Running LSTM ml5js example with my own training data
- 2018: Running ml5.js example - Simple LSTM Generator Example on a local browser
- Running ml5.js example - Simple Image Classification Example on a local browser
- Running spam data with RecurrentJS on a local browser
- Running a customized neural network on a local browser
- Running a PNG file with Synaptic.js on a local browser
- Running a jpg file with ConvNetJS on a local browser
