Skip to main content

Crossmodal Supervised Learning Toolkit using High-Performance Extreme Learning Machines over the audio-visual-textual data

Project description

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cerebrum, version 0.1.81
Filename, size File type Python version Upload date Hashes
Filename, size cerebrum-0.1.81-py2.py3-none-any.whl (45.3 kB) File type Wheel Python version 2.7 Upload date Hashes View
Filename, size cerebrum-0.1.81.tar.gz (23.0 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page