Abstract
Neural networks are associated with human brains as the human brain consists of 10,000 billion nerves. These nerves consist of neurons, which have some weight and receive signals, and these signals are processed and converted to the desired output. Similarly, neural networks are produced as a parallel gadget, which can perform computational assignments quicker than the regular system. The fundamental errand of neuron networks is to perceive examples and grouped capacities dependent on the estimation, improvement, and information bunching. So, neural networks are known as artificial neural network (ANN). This network fills in as a human cerebrum and age endeavours to take care of the overwhelming issues. ANN is parallel distributed processing systems or connective systems. In this chapter, we discuss the basic working of neurons, compare and contrast biological neural networks (BNN) with artificial neural networks (ANN), and discuss different types of learning systems used in ANN. We also discuss various applications of neural networks in real life.
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