Could you describe the "slice and dice" technique for the multidimensional model
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Could you describe the "slice and dice" technique for the multidimensional model?
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- You are developing a simulation model of a service system and are trying to create an input model of the customer arrival Process, You have the following four observations of the process of interest [86, 24,9, 50] and you are considering either an exponential distribution Of a uniform distribution for the model. Using the data to estimate any necessary distribution Parameters, write the steps to plot Q-Q plots for both cases i.e. exponential and uniform.Can you describe the "slice and dice" method employed in multidimensional models?Do you know how to describe the "slice and dice" method used by n-dimensional models?
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- question 2) what is the difference between correlation and convolution filtering methods? Briefly explain with a (your own) suitable exampleWhat is the problem encountered in Naive Baysian classification? How you get rid of the same? Write your answer with suitable example.Suppose you are designing a deep learning architecture to solve an object detection problem, where there areN numbers of objects and each object has M number of images. If the resolution of each image is 227 x 227 x 3, then answer the following questions: (a) Write down the name of the layers of your deep learning architecture including parameters dimension. (b) How many convolution layers will you use in your deep learning model and why? (c) Is it a good idea to use multiple fully connected layers? Motivate your answer