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1. A Bayesian network is shown for the variables paper Thickness, paper Alignment and Print Quality. The conditional probabilities are provided in the tables beside the nodes. Here, Thickness = {high or low}, Alignment = {straight or angled}, and Print Quality = {good or bad}.
Calculate the following quantities from the information in the Bayesian network.
a) P(Alignment = straight)
b) P(Alignment = straight | Print Quality = good)
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Write a program to mask bits D3D2D1D0 and to set bits D5D4 and to invert bits D7D6 of the AX register.
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Example of Weight training calculations: Through having calculated all the error values associated with such each unit like hidden and output then we can now transfer this inf
what is Throw-way model.
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