Artificial neural networks (ANNs) are computational models that are inspired by the structure and function of biological neural networks. ANNs consist of interconnected nodes that process and transmit information. There are various types of ANNs, including feedforward neural networks, recurrent neural networks, convolutional neural networks, and deep neural networks. Each type of ANN has a unique architecture and learning algorithm that enables it to perform specific tasks.
Artificial neural networks (ANNs) are computational models that are inspired by the structure and function of biological neural networks. ANNs consist of interconnected nodes that process and transmit information. There are various types of ANNs, including feedforward neural networks, recurrent neural networks, convolutional neural networks, and deep neural networks. Each type of ANN has a unique architecture and learning algorithm that enables it to perform specific tasks.
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Artificial neural networks (ANNs) are computational models that are inspired by the structure and function of biological neural networks. ANNs consist of interconnected nodes that process and transmit information. There are various types of ANNs, including feedforward neural networks, recurrent neural networks, convolutional neural networks, and deep neural networks. Each type of ANN has a unique architecture and learning algorithm that enables it to perform specific tasks.
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