A deep neural network (DNN) is a type of artificial neural network (ANN) that has many layers between the input and output layers. In contrast to shallow neural networks, which have only one or two hidden layers, deep neural networks can have dozens or even hundreds of hidden layers, allowing them to learn increasingly complex features and patterns in the input data.
DNNs are composed of interconnected nodes (also called artificial neurons) that process and transmit information in the form of numerical data. Each layer of a DNN performs a different type of computation on the input data, and the output of one layer is used as the input to the next layer. The final layer of the network produces the output, which is typically a classification or regression result. DNNs have been shown to be highly effective at solving a wide range of complex problems, including image and speech recognition, natural language processing, and game playing. However, training a DNN can be challenging due to the vanishing gradient problem, in which the gradient of the loss function becomes very small as it propagates back through the many layers of the network, making it difficult to update the weights of the network. To address this issue, various techniques have been developed, including the use of specialized activation functions, weight initialization strategies, and optimization algorithms, among others.
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