Deep neural network
Deep neural networks are under the broad category of deep learning. In contrast to neural networks, deep neural networks contain multiple hidden layers. The number of hidden layers can vary from problem to problem and needs to be optimized. R has many packages, such as darch, deepnet, deeplearning, and h20, which can create deep networks. However, I will use the deepnet package in particular and apply a deep neural network on DJI data. The package deepnet can be installed and loaded to the workspace using the following commands:
>install.packages('deepnet') >library(deepnet)
I will use set.seed() to generate uniform output and dbn.dnn.train() is used for training deep neural networks. The parameter hidden is used for the number of hidden layers and the number of neurons in each layer.
In the below example, I have used a three hidden layer structure and 3, 4, and 6 neurons in the first, second, and third hidden layers respectively. class.ind() is again used to convert...
