Artificial-Intelligence

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Artificial Neutral Networks (ANNs) are computational models inspired by the human brain’s neural networks. They consist of inter-connected nodes, or neurons, organized into layers : an input layers, one or more hidden layers and an output layers. Each connection between neurons has a weight that adjusts as learning progress allowing the network to adopt and improve its performance. ANNs are particularly effective in recognizing patterns making them valuable for tasks such as image and speech recognition, Natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of artificial intelligence by providing solutions to complex problems that traditional algorithms struggle with.

Artificial Neutral Networks (ANNs) are inspired by :