Difference between revisions of "Neural aesthetics"

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* Ethem Alpaydin, ''[http://gen.lib.rus.ec/book/index.php?md5=AAA419AE3AB733EBEAAFEC210E4CC2F5 Introduction to Machine Learning]'', MIT Press, 2004, 400 pp; [http://gen.lib.rus.ec/book/index.php?md5=7678204820FBE2E3D90A0C253EFF4F05 2nd ed.], 2009, 584 pp; [http://gen.lib.rus.ec/book/index.php?md5=68BB7D8062C844E3C11276546B55A751 3rd ed.], 2014, 640 pp.
 
* Ethem Alpaydin, ''[http://gen.lib.rus.ec/book/index.php?md5=AAA419AE3AB733EBEAAFEC210E4CC2F5 Introduction to Machine Learning]'', MIT Press, 2004, 400 pp; [http://gen.lib.rus.ec/book/index.php?md5=7678204820FBE2E3D90A0C253EFF4F05 2nd ed.], 2009, 584 pp; [http://gen.lib.rus.ec/book/index.php?md5=68BB7D8062C844E3C11276546B55A751 3rd ed.], 2014, 640 pp.
 
* Ian Goodfellow, Yoshua Bengio, Aaron Courville, ''[http://www.deeplearningbook.org/ Deep Learning]'', MIT Press, 2016.
 
* Ian Goodfellow, Yoshua Bengio, Aaron Courville, ''[http://www.deeplearningbook.org/ Deep Learning]'', MIT Press, 2016.
* Ethem Alpaydin, ''Machine Learning: The New AI'', MIT Press, 2016, 224 pp. [https://mitpress.mit.edu/books/machine-learning-1]
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* Ethem Alpaydin, ''[http://gen.lib.rus.ec/book/index.php?md5=1AB89B9FC1DEB7A47029FAD4089EE2FD Machine Learning: The New AI]'', MIT Press, 2016, 224 pp. [https://mitpress.mit.edu/books/machine-learning-1]
  
 
===Historization of deep learning===
 
===Historization of deep learning===

Revision as of 18:21, 16 November 2017

Partial results of the "hello world" test of a machine learning algorithm, automated recognition of handwritten digits, showing 125 test cases that the network got wrong. Each case is labeled by the network’s guess. The true classes are arranged in standard scan order. Source: Hinton et al 2006.
Basic structure of a neural network. Several techno-logical forms can be identified in the concept: scansion, that is discretisation or digitisation since the age of radio, TV, etc.; feedback loop, or the basic concept of cybernetics; and network form, here inspired by biological neurons. Diagram by Matteo Pasquinelli with Lukas Rehm, 2017. Source.

A resource on recent work between art/design and artificial neural networks in machine learning.

Events

Artists, designers

Initiatives

Literature, resources

Courses and textbooks for artists

Textbooks
Video lectures
  • Gene Kogan, "Machine Learning for Artists", course taught at ITP, New York University, New York, Spring 2016. 6 lectures, ~15 hours.
  • Gene Kogan, "The Neural Aesthetic", course taught at School Of Machines, Making, and Make-Believe, Berlin, Summer 2016. 8 lectures, ~18 hours.
Introductions
Resources
  • A.I. Experiments, a resource on machine learning art developed by Alexander Chen, creative director at Google Creative Lab, with Yotam Mann, C Christiansen, Jonas Jongejan, Gene Kogan, Kyle McDonald, et al. Launched on 16 Nov 2016.

Scientific introduction into deep learning

Historization of deep learning

Recent debates on artificial intelligence in the humanities

Art practice and criticism