Over the past two years, an academic medical center has purchased four community hospitals across the state with bed sizes ranging from 26 to 500. In trying to act on the organization’s strategic goals of providing greater economic and process efficiencies and increasing brand identification and market share, the chief HR officer has asked you to come up with a proposal for how to change the new-employee orientation and onboarding process to meet the following goals:

• Reduce costs of new-employee onboarding

• Promote a sense of system or brand identification, while still maintaining the culture of the community hospital where it is valuable

Currently each hospital has its own separate two-day new-employee orientation program and onboarding process, which the HR department at each of the hospitals is responsible for. Using the change management model in Exhibit 11.7 and your knowledge of the use of learning technology, answer the following questions:

1. Who would you suggest sponsor this change and be your active leadership core guiding this change?

2. What data would you want to collect?

3. What efficiencies would you expect to gain, and how would you realize them?

4. What resistance would you anticipate? How would you address it? 5. How would you go about getting buy-in for this change?

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Problem Description The MNIST database of handwritten digits (from 0 to 9) has a training set of 55,000 examples, and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixed-size image (28×28 pixels) with values from 0 to 1. You can use the following code with TensorFlow in Python to download the data. from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() Every MNIST data point has two parts: an image of a handwritten digit and a corresponding label. We will call the images ? and the labels ?. Both the training set and test set contain ? and ?. Each image is 28 pixels by 28 pixels and can be flattened into a vector of 28×28 = 784 numbers. As mentioned, the corresponding labels in the MNIST are numbers between 0 and 9, describing which digit a given image is of. In this assignment, we regard the labels as one-hot vectors, i.e. 0 in most dimensions, and 1 in a single dimension. In this case, the ?-th digit will be represented as a vector which is 1 in the ? dimensions. For example, 3 would be [0,0,0,1,0,0,0,0,0,0]. The assignment aims to build NNs for classifying handwritten digits in the MNIST database, train it on the training set and test it on the test set. Please read the following comments and requirements very carefully before starting the assignment: 1. The assignment is based on the content of Labs. 2. In Lecture 1, we talked about the use of training set, validation set and test set in machine learning. In the assignment, you are asked to train the NN on the training set and test the NN on the test set, instead of doing the two steps on the same data set as what was done in Lab 5. You do NOT need the validation set in the assignment. 3. In the assignment, the performance of a NN is measured by the its prediction accuracy in classifying images from the test set, i.e. number of the correctly predicted images / number of the images in the test set. 4. You are asked to model THREE NNs by changing the architecture. For example, you may change the number of layers, use different type of layers, and try various activation layers. 5. You are encouraged to repeatedly train and test your NNs with different parameter setting, e.g. learning rate. 6. Your report MUST at least contain the following content a. Names and student numbers of all group members;

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