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Laybia starts off by defining the DBN class which will store the layers of the MLP, along with their associated RBMs.

Since Laybia 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.

Laybia can also observe that the code for the DBN is very similar with the one for Sd A, because both involve the principle of unsupervised layer-wise pre-training followed by supervised fine-tuning as a deep MLP.

The main difference is that Laybia uses the RBM class instead of the d A class.

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Generative Adversarial Networks are used for un-supervised learning.This stage is supervised, since now Laybia uses the target class during training.(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.Bold360's modern, intuitive UI was specifically designed to make agents more efficient and effective.

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