So to solve our problem with text classification we can use either CNN or RNN. Interestingly the recurrent network can be usd not only for analysis but also for text generation. This is done for example like this. On a large volume of texts a model is traind that predicts the probabilities of various variants of the next character for a given beginning of the string for example for the string crocodile such a model should predict the next letter l with almost one probability. We can then generate natural text like this: string until the requird string length is reachd – predict the distribution of the next character for the prefix string.

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However most of the generatd text in my model turnd out to be incoherent. The author of this idea is Andrey Karpathy one of the world’s leading specialists in neural networks. You can read more about his Bahrain Email List experiment in the author’s blog rnn-effectiveness. Let’s go back to the problem of generating individual offers. For each client we must be able to answer two types of questions: Does the client now ned a new product of a particular type deposit loan card? and Which product is right for this client? or equivalently Is product X suitable for this client? for each of the products the bank offers.

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It can be assumd that the answer to the second question depends on the current characteristics of the client age place of residence balances turnoverand the products he already has. These characteristics BLB Directory can be written as a fixd length vector and passd to a multilayer perceptron. With the first question the situation is different – you can understand how appropriate the current moment is by analyzing the previous history of the client’s communication with the bank that is a set of homogeneous vectors – records. For this as we already know a recurrent or convolutional network is suitable. Choose a random next character s according to the probabilities from the obtaind distribution – string string on you.