ANN - Artificial (Neural Network) , A Network inspired from Human Brain. We try to mimic the working of real neurons in an artificial way.
Artificial network is a network of neurons connected to each other which retain the biological concept of neurons, which receive input, combine the input with their internal state (activation) and an optional threshold using an activation function, and produce output using an output function.
2. Connections and Weights
The network consists of connections, each connection providing the output of one neuron as an input to another neuron. Each link has a weight, which determines the strength of one node's influence on another, similar to a human brain.
ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input ,hidden and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output .In between them are zero or more hidden layers .
Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights and optional thresholds of the network to improve the accuracy of the result. This is done by minimizing the observed errors.
The learning rate defines the size of the steps that the model takes to adjust for errors in each observation. A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy.
If the network generates a “good or desired” output, there is no need to adjust the weights. However, if the network generates a “poor or undesired” output or an error, then the system alters the weights in order to improve subsequent results.
ANN have self-learning capabilities that enable them to produce better results as more data become available.
An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually.During this supervised phase, the network compares its actual output produced with the desired output. The difference between both outcomes is adjusted using backpropagation, ( this is what makes a network intelligent ).