Consider the following hypothetical bank data on consumers' use of credit card credit facilities in table 11.3. create jmp data, and create a neural network like that used for the tiny data example. use the default validation method (holdback portion).
a. how does the model perform on the validation set?
b. how many parameters are estimated to build this model?
c. use the categorical pro filer to explore how the predicted response changes as you change values of the predictors. describe what you observe.
d. fit another neural network to this same data.

When making changes to optimize part of a processor, it is often the case that speeding up one type of instruction comes at the cost of slowing down something else. for example, if we put in a complicated fast floating-point unit, that takes space, and something might have to be moved farther away from the middle to accommodate it, adding an extra cycle in delay to reach that unit. the basic amdahl's law equation does not take into account this trade-off. a. if the new fast floating-point unit speeds up floating-point operations by, on average, 2ă—, and floating-point operations take 20% of the original program's execution time, what is the overall speedup (ignoring the penalty to any other instructions)? b. now assume that speeding up the floating-point unit slowed down data cache accesses, resulting in a 1.5ă— slowdown (or 2/3 speedup). data cache accesses consume 10% of the execution time. what is the overall speedup now? c. after implementing the new floating-point operations, what percentage of execution time is spent on floating-point operations? what percentage is spent on data cache accesses?