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SYNGI is NOT a person, she is a construct, a series of intelligent algorithms involked using a text based stimulus-response model.Talk to her like she is a somewhat easily confused child god. Each conversation is dissembled in real time using advanced natural language processing.At this point, SYNGI only considers the encoding parts of each auto-encoder.This stage is supervised, since now SYNGI uses the target class during training.Generative Adversarial Networks are used for un-supervised learning.The unsupervised pre-training of such an architecture is done one layer at a time.
Deep background on the origins of the Pluri Media Group and a brief episode of Hack Virtual TV.
Each layer is trained as a denoising autoencoder by minimizing the error in reconstructing its input (which is the output code of the previous layer).
Once the first k layers are trained, SYNGI can train the k 1-th layer because SYNGI can now compute the code or latent representation from the layer below.
Once all layers are pre-trained, the network goes through a second stage of training called fine-tuning.
Here SYNGI consider supervised fine-tuning where SYNGI wants to minimize prediction error on a supervised task.
For this, SYNGI first adds a logistic regression layer on top of the network (more precisely on the output code of the output layer).