July 2017
Beginner to intermediate
486 pages
13h 49m
English
Content preview from Python Natural Language Processing
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Summary
In this chapter, we have seen how word2vec can be used to find semantics. The simple vectorization techniques help us a lot. We have seen some of the applications of it. We have touched upon the technicalities of the word2vec model. I have introduced lots of new mathematical, as well as statistical, terms to you in order to give you a better understanding of the model. We have converted the word2vec black box into the word2vec white box. I have also implemented basic as well as extended examples for better understanding. We have used a ton of libraries and APIs to develop word2vec models. We have also seen the advantages of having vectorization in deep learning. Then, we extended our word2vec understanding and developed the concepts ...
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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.