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 learnlinearrelationships between data. In order to be able to learn (you can also say approximate) any mathematical problem – no matter how complex – we useactivation 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.Dropoutis 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.

Kevin Feasel

2018-11-07

Data Science, Machine Learning, R