What is the basic of Neural Network?
ARTIFICIAL NEURAL NETWORKS?
In simple words software implementations of the neuronal structure of our brains. We don’t need to talk about the complex biology of our brain structures, but suffice to say, the brain contains neurons which are kind of like organic switches to do certain work in body. To produce desired results the neurons works which are interconnected in thousands in numbers they continuosly works, neural connections causing that outcome becomes strengthened.
Artificial neural networks attempt to simplify and mimic this brain behavior. They can be trained in a supervised or unsupervised manner. In a supervised ANN, the network is trained by providing matched input and output data samples, with the intention of getting the ANN to provide a desired output for a given input. For example spam and notspam in E-mails. Unsupervised learning in an ANN is an attempt to get the ANN to “understand” the structure of the provided input data “on its own”. The biological neuron is simulated in an ANN by an activation function.
As can be seen in the figure above, the function is “activated” i.e. it moves from 0 to 1 when the input x is greater than a certain value. The sigmoid function isn’t a step function however, the edge is “soft”, and the output doesn’t change instantaneously.
Nodes:
As mentioned previously, biological neurons are connected hierarchical networks, with the outputs of some neurons being the inputs to others. We can represent these networks as connected layers of nodes. Each node takes multiple weighted inputs, applies the activation function to the summation of these inputs, and in doing so generates an output.
Putting together the structure:
Hopefully the previous explanations have given you a good overview of how a given node/neuron/perceptron in a neural network operates. However, as you are probably aware, there are many such interconnected nodes in a fully fledged neural network. These structures can come in a myriad of different forms, but the most common simple neural network structure consists of an input layer, a hidden layer and an output layer. An example of such a structure can be seen below:

