Deep Neural networks


Deep Neural networks (DNN) is an Artificial Neural network with multiple hidden layers between the input layer and output layers.

Similar to shallow Artificial Neural networks, Deep Neural networkss can model complex non-linear relationships.

Deep Neural networks architectures generate compositional models where the object is expressed as a layered composition of primitives. The extra layers enable composition of features from lower layers, potentially modeling complex data with fewer units than a similarly performing shallow network.

Deep Neural networks architectures include many variants of a few basic approaches. Each architecture has found success in specific domains. It is not always possible to compare the performance of multiple architectures, unless they have been evaluated on the same data sets.

Deep Neural networkss are typically Feedforward Neural networks in which data flows from the input layer to the output layer without looping back.

Recurrent Neural networks (RNNs), in which data can flow in any direction, are used for applications such as language modeling. Long short-term memory is particularly effective for this use.

Convolutional Neural Network (CNNs) are used in computer vision. CNNs also have been applied to audio modeling for automatic speech recognition (ASR).


Artificial Intelligence

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