March 2018
Intermediate to advanced
320 pages
7h 53m
English
book
TensorFlow Deep Learning Projects
by Alexey Grigorev, Srinivas Kulkarni, Rajalingappaa Shanmugamani
Content preview from TensorFlow Deep Learning Projects
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Matrix factorization for recommender systems
In this section, we will go over traditional techniques for recommending systems. As we will see, these techniques are really easy to implement in TensorFlow, and the resulting code is very flexible and easily allows modifications and improvements.
For this section, we will use the Online Retail Dataset. We first define the problem we want to solve and establish a few baselines. Then we implement the classical Matrix factorization algorithm as well as its modification based on Bayesian Personalized Ranking.
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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.