The first neural network was conceived of by Warren McCulloch and Walter Pitts in 1943. They wrote a seminal paper on how neurons may work and modeled their ideas by creating a simple neural network using electrical circuits. This breakthrough model paved the way for neural network research in two areas: Biological processes in the brain, and the application of neural networks to artificial intelligence (AI). AI research quickly accelerated, with Kunihiko Fukushima developing the first true, multilayered neural network in 1975.
The original goal of the neural network approach was to create a computational system that could solve problems like a human brain. However, over time, researchers shifted their focus to using neural networks to match specific tasks, leading to deviations from a strictly biological approach. Since then, neural networks have supported diverse tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games, and medical diagnosis.
As structured and unstructured data sizes increased to big data levels, people developed deep learning systems, which are essentially neural networks with many layers. Deep learning enables the capture and mining of more and bigger data, including unstructured data.
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