What is neural network in artificial intelligence?

 

A neural network is an artificial intelligence technique that instructs computers to analyze data in a manner modelled after the human brain. It is a kind of artificial intelligence technique known as deep learning that makes use of interconnected neurons or nodes in a layered structure to mimic the human brain. Computers can use this to build an adaptive system that helps them continuously get better by learning from their failures. As a result, artificial neural networks make an effort to more accurately tackle challenging issues like summarizing documents or identifying faces. Neural networks can help computers make intelligent decisions with limited human assistance. This is why they can learn and model complex, nonlinear relationships between input and output data.

Neural networks have several use cases across many industries, such as the following:

• Medical image categorization for diagnosis
• Behavioral data analysis and social network filtering for targeted marketing
• Using historical financial instrument data to make financial predictions
• Energy demand and electrical load forecasts
• Process and quality control
• Chemical compound identification

We give four of the important applications of neural networks below.

• Computer vision
• Speech recognition
• Natural language processing
• Recommendation engines

Neural network architecture draws its influence from human brain function. Neurons, which are the building blocks of the human brain, communicate with one another via electrical signals to help people interpret information. Similar to this, a problem-solving artificial neural network is constructed of artificial neurons. Artificial neural networks are software programmes or algorithms that, at their heart, use computing systems to complete mathematical computations. Artificial neurons are software modules, also known as nodes.