About: DEEP BELIEF NETWORKS   Sponge Permalink

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Deep Belief Network(DBN) is a network architecture in machine learning. There were many shallow architectures which had an upper hand in Artificial intelligence. These shallow nueral networks architectures were not that much efficient in computational,statistical or human involvent needed to train the net. Still they enjoyed privilage over deep nets because of difficuty in training deep belief nets. The brake-through occured in 2006 when Hinton and his team developed a new deep learning layer wise greedy algorithm to train deep belief nets. Now they are of prime importance in the field of machine learning.

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  • DEEP BELIEF NETWORKS
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  • Deep Belief Network(DBN) is a network architecture in machine learning. There were many shallow architectures which had an upper hand in Artificial intelligence. These shallow nueral networks architectures were not that much efficient in computational,statistical or human involvent needed to train the net. Still they enjoyed privilage over deep nets because of difficuty in training deep belief nets. The brake-through occured in 2006 when Hinton and his team developed a new deep learning layer wise greedy algorithm to train deep belief nets. Now they are of prime importance in the field of machine learning.
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abstract
  • Deep Belief Network(DBN) is a network architecture in machine learning. There were many shallow architectures which had an upper hand in Artificial intelligence. These shallow nueral networks architectures were not that much efficient in computational,statistical or human involvent needed to train the net. Still they enjoyed privilage over deep nets because of difficuty in training deep belief nets. The brake-through occured in 2006 when Hinton and his team developed a new deep learning layer wise greedy algorithm to train deep belief nets. Now they are of prime importance in the field of machine learning. DBN learn data as layers of abstraction. The concept is very much similar how a human brain functions. According to studies of Hinton (Hinton et al. 2006) each layer or DBN is composed of restricted boltzmann machine. Each layer is trained one at a time where, activations from lower layer act as data for upper layer. This layer by layer training make use of Contrastive Divergence . Once layer by layer greedy pre training is done, back propogation is used for fine tuning. DBN is widely used for classification,dimensionality reduction,regression,information retrieval etc.
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