March 2018
Intermediate to advanced
272 pages
7h 53m
English
Content preview from Deep Learning Quick Reference
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Sequence-to-sequence model architecture
The key to understanding sequence-to-sequence model architecture is understanding that the architecture is built to allow the input sequence to vary in length from the output sequence. The entire input sequence can then be used to predict an output sequence of varying length.
To do that, the network is divided into two separate parts, each part consists of one or more LSTM layers responsible for half of the task. We discussed LSTMs back in Chapter 9, Training an RNN from scratch, if you'd like a refresher on their operation. We will learn about each of these two parts in the following sections.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.