January 2019
Intermediate to advanced
386 pages
11h 13m
English
book
Python Deep Learning - Second Edition
by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Content preview from Python Deep Learning - Second Edition
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Neural probabilistic language model
It is possible to learn the language model and, implicitly, the embedding function via a feedforward fully-connected network. Given a sequence of n-1 words (wt-n+1 , ..., wt-1), it tries to output the probability distribution of the next word wt (the following diagram is based on http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf):
Neural network language model that outputs the probability distribution of the word wt, given the words wt-1 ... wt-n+1. C is the embedding matrix
The network layers play different roles:
- The embedding layer takes the one-hot representation of the word wi and transforms ...
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