A neural network has the following components
* Neurons – A neuron is a mathematical function that attempts to mimic the behavior of a biological neuron. It calculates the weighted average of the data supplied and then sends the data through a nonlinear function, called the logistic function.
* Connections and weights – Connections link a neuron in one layer to another neuron in the same layer or another layer, as the name implies. A weight value is assigned to each connection. The strength of the relationship between the units is represented by a weight. The goal is to lower the weight number in order to diminish the chances of losing weight (error).
* Propagation – In a Neural Network, there are two propagation functions: forward propagation, which produces the “predicted value,” and backward propagation, which delivers the “error value.”
* Learning Rate – Gradient Descent is used to train neural networks. At each iteration, the derivative of the loss function is calculated in reference to each weight value using back-propagation and then subtracted from that weight. The learning rate determines how quickly or slowly the weight values of the model are updated.
