November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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Summary
In this chapter, we learned about recommendation systems. We learned about different kinds of recommendation systems such as collaborative filtering, content-based filtering, and hybrid systems. We used the Retailrocket dataset to create two models of our recommendation system, one with matrix factorization, and one using a neural network. We saw that the neural network model gave pretty good accuracy.
In the next chapter, we'll learn about object detection at large scale with distributed TensorFlow.
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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.