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The main goal of this work is to build and train multilayer NNs, train autoencoders to reduce the number of features for the classifiers and build and train deep networks (CNN and LSTM) for predicting or detecting the seizures.
Code for the analysis conducted in the paper "On the Importance of Hidden Bias and Hidden Entropy in Representational Efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines"
AutoenCODE is a Deep Learning infrastructure that allows to encode source code fragments into vector representations, which can be used to learn similarities.
Real-world application sized Neural Network. Implemented back-propagation algorithm with momentum, auto-encoder network, dropout during learning, least mean squares algorithm.