Neural Network Architecture
Neural Network Architecture refers to the arrangement of neurons and layers in a neural network. It determines the complexity and efficiency of the network in processing information. The architecture can be shallow or deep, with the former having only one hidden layer and the latter having multiple hidden layers. The choice of architecture depends on the nature of the problem being solved and the amount of data available. The architecture can also include different types of layers such as convolutional, recurrent, and pooling layers, each with a specific function in processing the input data. The architecture can be optimized through techniques such as hyperparameter tuning and regularization to improve the performance of the network.
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