Transcription of Neural Networks and Introduction to Bishop (1995) : Neural ...
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Neural Networks and Introduction to Bishop (1995) : Neural ...
1 Neural Networks and Introduction to Deep LearningNeural Networks and Introduction toDeep Learning1 IntroductionDeep learning is a set of learning methods attempting to model data withcomplex architectures combining different non-linear transformations. The el-ementary bricks of deep learning are the Neural Networks , that are combined toform the deep Neural techniques have enabled significant progress in the fields of soundand image processing, including facial recognition, speech recognition, com-puter vision, automated language processing, text classification (for examplespam recognition). Potential applications are very numerous.
2.4.1 Loss functions It is classical to estimate the parameters by maximizing the likelihood (or equivalently the logarithm of the likelihood). This corresponds to the mini-mization of the loss function which is the opposite of the log likelihood. De-noting the vector of parameters to estimate, we consider the expected loss function L( ) = E
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