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Artificial Intelligence

What is Neural Network?

Quick answer

A machine learning model made of layers of connected nodes, loosely inspired by the brain, that learns patterns by adjusting connection weights.

Neural Network, explained

A neural network takes input numbers, such as the pixel values of an image, and passes them through layers of artificial neurons. Each neuron multiplies its inputs by weights, adds them up, and applies a simple function. The final layer produces an output, such as the probability that the image shows a cat.

During training, the network compares its predictions with correct answers and uses an algorithm called backpropagation to adjust its weights slightly, repeating this many times until predictions improve. Networks with many layers are called deep neural networks, the basis of deep learning. Specialized designs include convolutional networks for images, recurrent networks for sequences, and transformers, which now dominate language and many other tasks.

Related terms

Deep Learning

A branch of machine learning that uses neural networks with many layers to learn patterns from large amounts of data.

Diffusion Model

A type of generative AI model that creates images, audio, or video by starting from random noise and refining it step by step.

Machine Learning

A branch of AI in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each case.

Mixture of Experts

A model design that splits a neural network into specialized sub-networks and activates only a few of them for each input.

Transformer

A neural network architecture, introduced in 2017, that uses attention mechanisms and underpins most modern large language models.

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