Understanding Neural Nets

David Smith links to a video which explains how neural networks do their thing:

In R, you can train a simple neural network with just a single hidden layer with the nnet package, which comes pre-installed with every R distribution. It’s a great place to start if you’re new to neural networks, but the deep learning applications call for more complex neural networks. R has several packages to check out here, including MXNetdarchdeepnet, and h2o: see this post for a comparison. The tensorflow package can also be used to implement various kinds of neural networks.

R makes it pretty easy to run one, though it then becomes important to understand regularization as a part of model tuning.

Related Posts

Patterns for ML Models in Production

Jeff Fletcher shows four patterns for productionalizing Machine Learning models, as well as some things to take care of once you’re in production: Operational DatabasesThis option is sometimes considered to be  real-time as the information is provided “as its needed,” but it is still a batch method. Using our telco example, a batch process can […]

Read More

Visualizing with Heatmaps in R

Anisa Dhana shows how you can create a quick heatmap plot in R: To give your own colors use the scale_fill_gradientn function.ggplot(dat, aes(Age, Race)) + geom_raster(aes(fill = BMI)) + scale_fill_gradientn(colours=c("white", "red")) This is a quick example using ggplot2 but there are other heatmap libraries available too.

Read More

Categories

March 2017
MTWTFSS
« Feb Apr »
 12345
6789101112
13141516171819
20212223242526
2728293031