Crossmodal Supervised Learning Toolkit using High-Performance Extreme Learning Machines over the audio-visual-textual data
Cerebrum’s purpose is getting continuous data inputs from different types of perceptions as memory sequences that triggered according to predefined threshold values and creating a complex time based relations between those memories by Crossmodal logic and training multiple Long Short-Term Memory Networks with this extracted data. Lastly creating outputs triggered by a stimuli, using pre-trained Artificial Neural Networks.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|cerebrum-0.1.81-py2.py3-none-any.whl (45.3 kB) Copy SHA256 hash SHA256||Wheel||2.7||Apr 20, 2016|
|cerebrum-0.1.81.tar.gz (23.0 kB) Copy SHA256 hash SHA256||Source||None||Apr 20, 2016|