November 2016
Beginner to intermediate
941 pages
21h 55m
English
book
Python: Real World Machine Learning
by Prateek Joshi, John Hearty, Bastiaan Sjardin, Luca Massaron, Alberto Boschetti
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Building Conditional Random Fields for sequential text data
The Conditional Random Fields (CRFs) are probabilistic models used to analyze structured data. They are frequently used to label and segment sequential data. CRFs are discriminative models as opposed to HMMs, which are generative models. CRFs are used extensively to analyze sequences, stocks, speech, words, and so on. In these models, given a particular labeled observation sequence, we define a conditional probability distribution over this sequence. This is in contrast with HMMs where we define a joint distribution over the label and the observed sequence.
Getting ready
HMMs assume that the current output is statistically independent of the previous outputs. This is needed by HMMs to ensure ...
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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.