February 2020
Intermediate to advanced
328 pages
8h 19m
English
Content preview from Deep Learning with R Cookbook
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How it works...
To build any language model, we need to clean the input text and break it into tokens. Tokens are individual words, and breaking text into its different words is called tokenization. By default, the keras tokenizer splits the corpus into a list of tokens (" " is used for splitting sentences into words), removes all punctuation, converts the words into lowercase, and builds an internal vocabulary based on the input text. The vocabulary that's generated by the tokenizer is an indexed list where the words are indexed by their overall frequency in the dataset. In this recipe, we saw that in the nursery rhyme, "and" is the most frequent word, while "up" is the 5th most frequent word. There are 37 unique words in total.
In step ...
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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.