July 2017
Beginner to intermediate
715 pages
17h 3m
English
Content preview from Java: Data Science Made Easy
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Configuring an RBM
We will examine two different RBM configurations. The first one is minimal and we will see it again in Deep autoencoders. The second uses several additional methods and provides more insights into the various ways it can be configured.
The following statement creates a new layer using the RBM.Builder class. The input is computed based on the number of rows and columns of an image. The output is large, containing 1000 neurons. The loss function is RMSE_XENT. This loss function works better for some classification problems:
.layer(0, new RBM.Builder() .nIn(numRows * numColumns).nOut(1000) .lossFunction(LossFunctions.LossFunction.RMSE_XENT) .build())
Next is a more complex RBM. We will not detail each of these methods ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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