February 2018
Beginner to intermediate
258 pages
5h 47m
English
Content preview from R Deep Learning Projects
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RNNs from scratch in R
The purpose of this section is to show you how you can implement recurrent neural networks from bare bones in R. This is perhaps not the optimal solution for a number of reasons, but it is a great way to get started in deep learning.
There are many plug and play frameworks like H2O, MXNet, TensorFlow, or Keras, that have compatibility with R. Our goal is to focus on the understanding of the algorithm rather than a particular API, although we will include an example using Keras. This is for two reasons, at the time of writing, the compatibility with R suffers from growing pains and we encountered many errors and issues with the different packages. On the other hand, even the stable versions of such packages have ever-changing ...
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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.