What is neural network and how it relates to AI?

A particular kind of machine learning algorithm called a neural network is created to mimic the structure and operation of the human brain. Neurons, the connecting nodes that make up neural networks, are arranged into layers. Each neuron takes in information from other neurons via input signals, analyzes it, and then sends out an output signal to other neurons.

Since they can learn from data and generate predictions based on that data, neural networks play a significant role in artificial intelligence (AI). Data is sent into a conventional neural network where it is processed by the neurons in each layer. The network's projected outcome is the last layer's output.

It is possible to train neural networks using supervised learning. In supervised learning, a set of labeled training data is delivered to the neural network, where each input is connected to a predetermined output. Based on the discrepancies between its anticipated outputs and the true outputs in the training data, the neural network modifies its weights and biases. The neural network gains the ability to predict new data with accuracy over time.

Many AI applications, such as voice and image recognition, natural language processing, and autonomous vehicles, utilise neural networks. In jobs involving pattern recognition or data processing, where conventional rule-based programming techniques may be difficult or impossible to execute, they are very helpful.