September 2017
Beginner to intermediate
270 pages
5h 53m
English
Content preview from Neural Networks with R
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Summary
In this chapter, we saw RNNs and how to use internal memory for their processing. We also covered CNNs, which are standardized neural networks mainly used for image recognition. For RNNs, we studied some sample implementations in R.
We learned how to train, test, and evaluate an RNN. We also learned how to visualize the RNN model in an R environment. We discovered the LSTM model. We introduced the concepts as CNN and a common CNN architecture: LeNet.
In the next chapter, we will see more use cases involving R implementations of neural networks and deep learning.
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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.
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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.