train an artificial neural network using CIFAR10 dataset. You can get the dataset from Keras similar to mnist dataset

Suppose you are fitting a neural network with one hidden layer to a training set. You find that the cross validation error Jcv (0) is much larger than the training error Jtrain (0). Is increasing the number of hidden units likely to help? CV Yes, because it is currently suffering from high bias. Ob. Yes, because this increases the number of parameters and lets the network represent more complex functions. Oc. No, because it is currently suffering from high variance, so adding hidden units is unlikely to help. Od. No, because it is currently suffering from high bias, so adding hidden units is unlikely to help.

You are developing a simulation model of a service system and are trying to create aninput 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 distributionOf a uniform distribution for the model. Using the data to estimate any necessary distributionParameters, write the steps to plot Q-Q plots for both cases.

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 distributionor 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 uniform and exponential distribution.  Write the steps clearly. Thanks.

2. Can you design a binary classification experiment with 100 total population (TP+TN+FP+ FN), with precision (TP/(TP+FP)) of 1/2, with sensitivity (TP/(TP+FN)) of 2/3, and specificity (TN/(FP+TN)) of 3/5? (Please consider the population to consist of 100 individuals.)