April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Summary
In this chapter, we took a deeper look into more complex text processing and explored MLlib's text feature extraction capabilities, in particular the tf-idf term weighting schemes. We covered examples of using the resulting tf-idf feature vectors to compute document similarity and train a newsgroup topic classification model. Finally, you learned how to use MLlib's cutting-edge Word2Vec model to compute a vector representation of words in a corpus of text and use the trained model to find words with contextual meaning that is similar to a given word. We also looked at using Word2Vec with Spark ML
In the next chapter, we will take a look at online learning, and you will learn how Spark Streaming relates to online learning models.
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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.