Neural Networks and Deep Learning: Neural Networks & Deep Learning, Deep Learning, Big Data by Pat Nakamoto

Author:Pat Nakamoto [Nakamoto, Pat]
Language: eng
Format: epub
Published: 2018-06-23T23:00:00+00:00

you can see that:

where the <·>data operator is the expected value in the distribution of data (the data set), while <·> model is the expected value that would be obtained through sampling from the BM after the state of equilibrium has been reached. Therefore, what you should do to perform a gradient ascent is to generate examples from the BM after having reached the equilibrium obtaining the values of the part on the right of the equation, and after that to modify wij based on the values obtained from this difference:

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