Difference between revisions of "Machine Learning Experiments"

From Software Studies
Jump to navigation Jump to search
Line 12: Line 12:
*Ed Finn, "Building the Star Trek Computer," in [https://mitpress.mit.edu/books/what-algorithms-want What Algorithms Want], MIT Press, 2017, pp. 57-85.
*Ed Finn, "Building the Star Trek Computer," in [https://mitpress.mit.edu/books/what-algorithms-want What Algorithms Want], MIT Press, 2017, pp. 57-85.
*Mackenzie Adrian, [https://mitpress.mit.edu/books/machine-learners Machine Learners:Archaeology of a Data Practice], MIT Press, 2017
*Mackenzie Adrian, [https://mitpress.mit.edu/books/machine-learners Machine Learners:Archaeology of a Data Practice], MIT Press, 2017


==Technical explanation on Machine Learning==
==Technical explanation on Machine Learning==

Revision as of 14:15, 21 November 2017

Introduction to Machine Learning

Cultural matters with Machine Learning

Technical explanation on Machine Learning

RNN focus

Neural Network

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

Projects

Text related

Artworks

Exhibition

Workshop

Demo/Experimental Projects

Learning resource

Teaching Machine Learning

Experiments/Tests

  • Running spam data with RecurrentJS in a local browser (Winnie)
Running spam data with RecurrentJS
  • Running a jpg file with ConvNetJS in a local browser (Winnie)
Learning a jpg file with ConvNetJS
  • Running a PNG file with Synaptic.js in a local browser (Winnie)
Learning a png file with Synaptic
  • Running a customized neural network in a local browser (Winnie)
Learning XOR with Synaptic