February 2017
Intermediate to advanced
370 pages
8h 11m
English
Content preview from TensorFlow Machine Learning CookbookBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 7. Natural Language Processing
Here we will cover an introduction to working with text in TensorFlow. We start by introducing how word embeddings work and using the bag of words method, then we move on to implementing more advanced embeddings such as Word2vec and Doc2vec:
- Working with bag of words
- Implementing TF-IDF
- Working with Skip-gram Embeddings
- Working with CBOW Embeddings
- Making Predictions with Word2vec
- Using Doc2vec for Sentiment Analysis
As a note, the reader may find all the code for this chapter online at https://github.com/nfmcclure/tensorflow_cookbook.
Introduction
Up to this point, we have only considered machine learning algorithms that mostly operate on numerical inputs. If we want to use text, we must find a way to convert the ...
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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.