February 2018
Beginner to intermediate
258 pages
5h 47m
English
Content preview from R Deep Learning Projects
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GRU
Gated recurrent units (GRUs) share a similar design philosophy to LSTM layers. They consist of:
- A hidden state, h
- An internal state, s
- An update gate, z
- A reset gate, r
The updates are given by the following recurrence relations:
The reset gate tells us how to put together the input with the previous memory. The update gate defines how much of the previous memory we should keep for the next iteration. This helps the network to forget useless things, and create connections with newer evidence. The resulting network has no hidden memory (c) which is independent of the internal state, as in the case with LSTMs. They also have input and ...
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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.