March 2018
Intermediate to advanced
272 pages
7h 53m
English
Content preview from Deep Learning Quick Reference
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Characters versus words
Sequence-to-sequence models can be built at either the character level or the word level. A word-level sequence-to-sequence model will take words as the atomic unit of the input, and a character-level model will take characters as the atomic unit of the input.
So, which should you use? Typically, the best results are obtained from word-level models. That said, predicting the most probable next word in a sequence requires a softmax layer as wide as the vocabulary of the problem. This results in a very wide, highly dimensional problem.
Character-level models are much smaller. There are 26 letters in the alphabet but there are about 171,000 English words in common use.
For the problem we present in this chapter, I'll ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.