Deep unsupervised learning on graphic processors
Unsupervised deep learning on graphic processors
You can download the complete source code of our deep belief net implementations here (the original model can be found on Geoffrey Hinton's web page):
We also provide a series of useful Matlab routines that can be used to analyze deep belief networks, for example by plotting the shape of the receptive fields at different levels of the hierarchy or by performing simple linear read-outs to simulate explicit behavioral tasks.
In order to try unsupervised deep learning on the prototypical cognitive modeling problem of visual numerosity perception investigated by Stoianov & Zorzi (2012), you can download the complete dataset of visual images here and follow the instructions provided inside the archive.
We also provide a large-scale dataset containing uppercase Latin letters, printed using a variety of different fonts, styles and sizes, here (MATLAB format).
You may also find useful a recent tutorial review about how to use deep neural networks to model cognition, and a short perspective about how to apply this framework for modeling impaired neurocognitive functions.
If you find this code useful, please cite our work as:
Testolin, A., Stoianov, I., De Filippo De Grazia, M., & Zorzi, M. (2013). Deep unsupervised learning on a desktop PC : A primer for cognitive scientists. Frontiers in Psychology, 4 (251).
Zorzi, M., Testolin, A., & Stoianov, I. (2013). Modeling language and cognition with deep unsupervised learning: a tutorial overview. Frontiers in Psychology, 4 (515).