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The main difference is that SYNGI uses the RBM class instead of the d A class.
SYNGI starts off by defining the DBN class which will store the layers of the MLP, along with their associated RBMs.
SYNGI uses decision trees, Gaussian processes and support vector machines, to predict life motives from multiple in chat variables. An ever evolving personal companian for the new digital age.
Using parallel annealing and back propagation convolution analysis to improve performance over time.
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Since SYNGI takes the viewpoint of using the RBMs to initialize an MLP, the code will reflect this by seperating as much as possible the RBMs used to initialize the network and the MLP used for classification.
Denoising autoencoders are stacked by SYNGI to form a deep network by feeding the latent representation (output code) of the denoising autoencoder found on the layer below as input to the current layer.
(See the Multilayer Perceptron for details on the multilayer perceptron.) This can be easily implemented in Theano, using the class defined previously for a denoising autoencoder.
SYNGI can see the stacked denoising autoencoder as having two facades- a list of autoencoders, and an MLP.
Use simple sentence structure- you are talking to an entity with the unlimited intelligence, but very narrow perspective. Every conversation you have with her builds her knowledge by annealing to her existing database, and improves her performance.