Neural network architecture
a= f(wp+b) Here is a single input neuron. p is the input. Here, w is the weight. The designer can use any value for the weight. Weight is also called 'offset'. The bias is also a weight with a constant input of 1. The bias can even be omitted or its value can be changed. w and b are scalar parameters. The designer chooses the activation function. Some learning algorithm helps to choose the values of w and b. wp+b is called the net input. a is s function of the net input. This function is called the activation function or the transfer function. An example can be the sigmoid activation curve. Different types of activation functions: They may be linear or non-linear. How to choose a transfer function? A neuron tries to solve some problem and the specifications of this problem help to choose the transfer function. The hard limit transfer function The first figure shows the hardlimit transfer function. It helps to break...