Neural networks are mathematical models that use learning algorithms inspired by the brain to store information. NN are built like computing systems with interconnected nodes that are similar to work of the human brain. NN use algorithms that can recognize hidden patterns and correlations in raw data, cluster and classify it. NN learn and improve themselves.

History of neural networks

The first step toward artificial neural networks was conceived of by McCulloch, a neurophysiologist, and a young mathematician, Walter Pitts, in 1943. They wrote a paper on how neurons might work and modeled their ideas by creating a simple neural network using electrical circuits.

Why we need neural networks

Neural networks can help people solve complex problems in many situations. NN can perform the complex tasks easily that are difficult for us. They give accurate results, can learn and model the relationships, make generalizations and inferences, reveal hidden relationships, patterns and predictions. Neural networks can help in many areas:

Trading and investment.

Credit card fraud detection.

Legal analysis.

Character and voice recognition.

Medical and disease diagnosis.

Job search.

Financial predictions for stock prices, currency, options, futures, bankruptcy and bond ratings.

Robotic control systems.

And many others.