Very vaguely inspired by the biological network of neurons residing in our brain, artificial neural networks (ANNs) are made up of a collection of units named artificial neurons that are organized into the following three types of layers:
- Input layer
- Hidden layer
- Output layer
The basic artificial neuron works (see the following image) by calculating a dot product between an input and its internal weights, and the results is then passed to a nonlinear activation function f (sigmoid, in this example). These artificial neurons are then connected together to form a network. During the training of this network, the aim is to find the proper set of weights that will help with whatever task we want our network to do:
Next, we have an example of a 2-layer feed forward artificial neural network. Imagine that the connections between neurons are the weights...
