What is a Neural Network?

A neural network is a type of machine learning algorithm modeled after the structure and function of the human brain. It is composed of multiple interconnected “neurons” that process and transmit information.

At a high level, a neural network takes in inputs, performs a series of calculations on them, and produces an output. These inputs can be anything from raw data such as images or text, to more abstract concepts such as the relationship between different variables.

The calculations performed by the neural network are called “activations,” and they are determined by the network’s weights and biases. The weights and biases are parameters that the neural network learns through training, and they determine how much influence each input has on the output and how the network processes the input data.

There are many types of neural networks, but they all have the same basic structure. They consist of an input layer, one or more hidden layers, and an output layer. The input layer receives the raw input data, and each subsequent layer processes the data further until it reaches the output layer, which produces the final result.

Training a neural network involves feeding it a large dataset and adjusting the weights and biases through a process called backpropagation. During training, the neural network makes predictions based on the input data, and the error between the predicted output and the true output is used to update the weights and biases to reduce the error. This process is repeated until the neural network achieves a satisfactory level of accuracy on the training data.

Neural networks have a wide range of applications, including image and speech recognition, natural language processing, and even playing games such as chess and Go. They have become an important tool in the field of artificial intelligence and are used in a variety of industries, including healthcare, finance, and transportation.