Skip to Content

Neural Network

A neural network is a machine learning model that mimics the function and structure of the human brain. It uses interconnected nodes in a layered structure to process data, make decisions, and learn from mistakes.

Each node performs a simple calculation, similar to how a biological neuron fires an electrical signal. These nodes are not programmed with specific instructions, but instead learn through training on data.

The network is inspired by the way biological neurons signal one another. It consists of an input layer, one or more hidden layers, and an output layer. Each node connects to another, has an associated weight and threshold, and sends data to the next layer if its output is above the specified threshold value.

The input layer receives data, like pixels in an image or words in a sentence. Hidden layers process and transform the data, extracting features and patterns. The output layer produces a result, like classifying an image or predicting the next word in a sentence. Weights and biases are values that determine how the nodes connect and influence each other. These are adjusted during training where the network is fed large amounts of data.

Neural networks can be used for various tasks such as:

  • Pattern recognition — Used in image recognition, speech recognition, and natural language processing. 
  • Predictions — Used in weather forecasting, stock market analysis, and recommendation systems.
  • Generating content — Used to create music, written text, images, videos, and translate languages.

There are many different types of neural networks, including feedforward neural networks, self attention networks, convolutional neural networks, and recurrent neural networks. Arguably the most famous one is the general pretrained transformer. It’s a unique type of neural network that consists of self-attention and feed-forward neural networks. It’s used in ChatGPT.

Related Terms

Learn More About Neural Networks