The following table consists of training data from an employee database The data have been generalized For example, 31 35 for age represents the age range of 31 to 35 For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that row Let status be the class label attribute (1) Using the multilayer feed forward neural network, show the weight values after one iteration of the backpropagation algorithm Indicate your initial weight values and biases, and the learning rate used (2) Given a training instance (sales, senior, 31 35, 46K 50K), which class the ANN will classify the data to Need full detailed answer Thanks count department sales 30 sales 40 sales 40 20 5 status age salary senior 31 35 46K 50K junior 26 30 26K 30K junior 31 35 31K 35K junior 21 25 46K 50K senior 31 35 66K 70K junior 26 30 46K 50K senior 41 45 66K 70K senior 36 40 46K 50K junior 31 35 41K 45K senior 46 50 36K 40K junior 26 30 26K 30K 3 systems systems systems systems marketing marketing secretary secretary 3 10 4 4 6

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The following table consists of training data from an employee database. The data have been generalized. For example, 31... 35 for age represents the age

The following table consists of training data from an employee database. The data have been generalized. For example, 31... 35 for age represents the age range of 31 to 35. For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that row. Let status be the class label attribute.

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(1) Using the multilayer feed-forward neural network, show the weight values after one iteration of the backpropagation algorithm. Indicate your initial weight values and biases, and the learning rate used.
(2) Given a training instance (sales, senior, 31. . . 35, 46K. . . 50K), which class the ANN will classify the data to.

Need full detailed answer. Thanks

count department sales 30 sales 40 sales 40 20 5 status age salary senior 31...35 46K...50K junior 26...30 26K... 30K junior 31...35 31K...35K junior 21...25 46K...50K senior 31...35 66K...70K junior 26...30 46K...50K senior 41...45 66K...70K senior 36...40 46K... 50K junior 31...35 41K...45K senior 46...50 36K...40K junior 26...30 26K... 30K 3 systems systems systems systems marketing marketing secretary secretary 3 10 4 4 6