Neural Networks | 6th | week 2 |.
An artificial neural network is a collection of smaller units called neurons, which are computing units modeled on the way the human brain processes information. Artificial neural networks borrow some ideas from the biological neural network of the brain, in order to approximate some of its processing results. These units or neurons take incoming data like the biological neural networks and learn to make decisions over time. Neural networks learn through a process called backpropagation. Backpropagation uses a set of training data that match known inputs to desired outputs. First, the inputs are plugged into the network and outputs are determined. Then, an error function determines how far the given output is from the desired output. Finally, adjustments are made in order to reduce errors. A collection of neurons is called a layer, and a layer takes in an input and provides an output. Any neural network will have one input layer and one output layer. It will also have one or more hidden layers which simulate the types of activity that goes on in the human brain. Hidden layers take in a set of weighted inputs and produce an output through an activation function. A neural network having more than one hidden layer is referred to as a deep neural network. Perceptrons are the simplest and oldest types of neural networks. They are single-layered neural networks consisting of input nodes connected directly to an output node. Input layers forward the input values to the next layer, by means of multiplying by a weight and summing the results.