Explaining Neural Networks With H2O

Shirin Glander explains some of the concepts behind neural networks using H2O as a guide:

Before, when describing the simple perceptron, I said that a result is calculated in a neuron, e.g. by summing up all the incoming data multiplied by weights. However, this has one big disadvantage: such an approach would only enable our neural net to learn linearrelationships between data. In order to be able to learn (you can also say approximate) any mathematical problem – no matter how complex – we use activation functions. Activation functions normalize the output of a neuron, e.g. to values between -1 and 1, (Tanh), 0 and 1 (Sigmoid) or by setting negative values to 0 (Rectified Linear Units, ReLU). In H2O we can choose between Tanh, Tanh with Dropout, Rectifier (default), Rectifier with Dropout, Maxout and Maxout with Dropout. Let’s choose Rectifier with Dropout. Dropout is used to improve the generalizability of neural nets by randomly setting a given proportion of nodes to 0. The dropout rate in H2O is specified with two arguments: hidden_dropout_ratios, which per default sets 50% of hidden (more on that in a minute) nodes to 0. Here, I want to reduce that proportion to 20% but let’s talk about hidden layers and hidden nodes first. In addition to hidden dropout, H2O let’s us specify a dropout for the input layer with input_dropout_ratio. This argument is deactivated by default and this is how we will leave it.

Read the whole thing and, if you understand German, check out the video as well.

Related Posts

Inline Operators In R With wrapr

John Mount shows how to use inline operators in R with the wrapr package: The above code is assuming you have the wrapr package attached via already having run library('wrapr'). Notice we picked R-related operator names. We stayed away from overloading the + operator, as the arithmetic operators are somewhat special in how they dispatch in R. The goal wasn’t […]

Read More

Feature And Text Classification Using Naive Bayes In R

I wrap up my series on the Naive Bayes class of algorithms, finally writing some code along the way: Now we’re going to look at movie reviews and predict whether a movie review is a positive or a negative review based on its words. If you want to play along at home, grab the data set, […]

Read More

Categories

November 2018
MTWTFSS
« Oct Dec »
 1234
567891011
12131415161718
19202122232425
2627282930