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Pick one or more of the following; ADAPT, ORDER or DEAL concepts from chapter 1. Write a one page (double spaced) paper discussing and describing the concept or concepts of your choice. You may use definitions or scenarios in your writing. The goal of this paper is to gain a level of understanding of the concepts (ADAPT, ORDER and DEAL) that can then be put to use in the classes final project.

➡️Please give a scenario on how your choice relates to Ethics give. Also give examples In to show your understanding of the concept.

➡️*** NOTE  I had someone from homework market attempt do the assignment for me. However I turned the assignment in and I received a low score because they didn’t explain how Adapt Relates to Ethics (or Philosophy202). The teacher is giving me another opportunity to redo the assignment.

➡️**PLEASE SEE ATTACHED PAPER. It’s the first paper that was wrong. That way you’ll know what NOT to do.

➡️➡️➡️ALSO I ATTACHED 4 pages out the book that explains what each concept means.

➡️******Also READ THE INSTRUCTORS FEEDBACK below about the attached assignment that I submitted that was incorrect.

⬇️

Comments From instructor on how the assignment was done incorrectly:

This is a strange assignment. You seem to focus on the word “Adapt” generally, but the question was about the acronym ADAPT- Attention, Dialogue, Assumptions, Proposals, Test used to aid in moral decision making and described in section 1.3.1 of the assigned reading. You should always be weary of an assignment for an ethics class that says nothing about ethics. If you would like to re-do the assignment, then just resubmit by Friday and email me when it is completed.

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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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