Neural Networks with R

Step-by-step illustration of a neuralnet and an activation function

We shall take a step-by-step approach to understand the forward and reverse pass with a single hidden layer. The input layer has one neuron and the output will solve a binary classification problem (predict 0 or 1). In the following figure is shown a forward and reverse pass with a single hidden layer:

Next, let us analyze in detail, step by step, all the operations to be done for network training:

1. Take the input as a matrix.
2. Initialize the weights and biases with random values. This is one time and we will keep updating these with the error propagation process.
3. Repeat the ...