Neural networks are a sub-field of artificial intelligence and machine learning that models biological neural networks. They aim to replicate the way the human brain functions, learning from experience and recognizing patterns in data. Neural networks are composed of input and output layers, as well as multiple hidden layers. Each layer is composed of nodes or neurons, which are interconnected and share weights.
A neural network is used to approximate a desired function by training it on a large set of inputs with the goal of obtaining a general rule or mapping from the inputs to their corresponding outputs. Inputs are typically numbers, but they can also be images or any other type of data. Outputs could be values, categories, or any type of data.
The structure of a neural network is determined by the user, as the user must specify the number of layers and nodes for each layer. Each node applies a specific function on the input, and the output of that node is the input to the next node. This chain of nodes is referred to as a forward propagation path. During the training phase, the weights of the connections between the nodes are adjusted based on the feedback from the output layer. This process, known as backpropagation, enables the neural network to learn from its mistakes.
Neural networks are used in a variety of applications, from handwriting recognition to image classification. They are now seen as a powerful tool for understanding large datasets and making predictions. As computing power and data availability continue to grow, neural networks are becoming increasingly more popular and their applications are becoming increasingly widespread.
