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What is a Neural Network?

by Iwan Price-Evans on Artificial Intelligence • June 1, 2022

Artificial neural networks are computational models inspired by biological neural networks. They learn from experience and make decisions based on what they have learned. They are the basis for some types of artificial intelligence.

Applications Of Neural Networks

Neural networks are used in many different applications, including self-driving cars, speech recognition, and computer vision. They're also being developed for use in medical diagnosis and treatment.

How Does A Neural Network Work?

An artificial neural network consists of nodes connected by weighted edges. Nodes represent neurons, and edges represent connections between them. Each node receives input signals from other nodes, processes these signals, and then sends output signals to other nodes. These output signals are used to update the weights of the edges connecting the nodes. In this way, the model learns from its experiences.

Types Of Neural Network

There are three main types of neural networks: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training data with labels. Unsupervised learning does not require labeled data. Reinforcement learning uses rewards and punishments to train the network.