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NEW QUESTION: 1
DRAG DROP
Place the steps in sequence.
Refer to the Exhibit.

Match the phase of the Application Life cycle Management roadmap with the appropriate number
in the exhibit.

Answer:
Explanation:

Explanation:
Reference: http://www.whiteboxqa.com/StudentMaterial/Guides/Quality%20Center%20User%20Guide%2011. pdf page 27

NEW QUESTION: 2
Managing an online event or reverse auctions includes traditional sourcing steps as well as procedures unique to the event. Which of the following is not a component of step ONE which is "create the category profile"?
A. Select appropriate techniques
B. Analyze the supplier base
C. Define the category
D. Confirm user requirements
Answer: A
Explanation:
Managing an online event or reverse auctions includes traditional sourcing steps as well as procedures unique to the event. "Select appropriate techniques" is not a component of step ONE which is "create the category profile". It is actually part of step two which is generate supplier profiles. All the remaining are correct: Define the category, Confirm user requirements, Analyze the supplier base.

NEW QUESTION: 3
You are developing an ASP.NET MVC web application that contains the following HTML.
<table id= "customer" ></table>
You also have an ASP.NET Web API application that contains a call for retrieving
customers.
You must send and retrieve the data in the most compact format possible.
You need to update the HTML for the customers table to contain data from the Web API
application.
Which script segment should you use?

A. Option D
B. Option A
C. Option C
D. Option B
Answer: B

NEW QUESTION: 4
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
A. Use feature engineering to add features for eyes, noses, and mouths to the input data.
B. Use K-means Clustering to detect faces in the pixels.
C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
Answer: C
Explanation:
Explanation
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning.
So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

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