8: Learning Representations by Back-Propagating Errors By David E. Rumelhart, Geoffrey E. Hinton,
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a...
We describe a mew learning procedure, back-prepagation, for networks of seurone-like units.The procedure repeatedly adjusts theweights of theconmections in themetworkse as to minimize a measureof the difference hetween the actual output vectorof the met and the desired output vector.
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector.
Describes back-propagation, which is a set of learning rules for neural networks. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector.
Learning internal representations by error propagation. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition.
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Human learning begins at birth and may even start before birth [5] and continues until death as a consequence of ongoing interactions between people and their environment.
Learning is a relatively lasting change in behavior resulting from observation and experience. It is the acquisition of information, knowledge, and problem-solving skills. When you think of learning, it's easy to focus on formal education that takes place during childhood and early adulthood.