Machine Learning Experiments

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
• 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

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

Text related

• 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 related

• Image paint: 1/ ConvNetJS Library by Andrej Karpathy 2/ Synaptic.js by Synaptic

Projects

Text related

• THE TEXT-GENERATING RNN DEMO by Ilya Sutskever
• Handwriting Generation Demo by Alex Graves
• Obama RNN by Samim
• RNN Bible on Twitter

Artworks

• 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 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

• Machine Learning for Introductory Physical Computing Curricula by Ali Momeni

Experiments/Tests

• Running spam data with RecurrentJS in a local browser (Winnie)

• Running a jpg file with ConvNetJS in 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)