Deep Neural Networks


Deep Neural Networks (DNNs) are a class of artificial neural networks (ANNs) that are composed of multiple layers of interconnected nodes, allowing for the creation of complex models capable of learning and performing tasks with high accuracy. DNNs are designed to automatically learn hierarchical representations of data, with each layer of nodes learning increasingly abstract features from the input data. This allows DNNs to excel at tasks such as image and speech recognition, natural language processing, and predictive analytics. DNNs are trained using large datasets and backpropagation, a process that adjusts the weights of the connections between nodes to minimize the difference between the predicted output and the actual output. DNNs have been used to achieve state-of-the-art results in a wide range of applications, including computer vision, speech recognition, and natural language processing.


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