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NEW QUESTION: 1
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 deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C. Use K-means Clustering to detect faces in the pixels.
D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
Answer: B
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
NEW QUESTION: 2
A. Azure Internet Load Balancer (ILB)
B. Azure Traffic Manager
C. Azure Load Balancer
D. Azure Application Gateway
Answer: C
Explanation:
References:
https://docs.microsoft.com/en-us/azure/application-gateway/application-gateway-introduction
NEW QUESTION: 3
Consider the widget template snippet, which uses Knockout's containerless control flow syntax based on comment tags:
Under what condition will <li>Item #2</li> be displayed?
A. Item #2 will never be displayed
B. Item #2 will always be displayed
C. If the shopper has a product in his/her cart
D. if the Product view model is available on the page
Answer: C
NEW QUESTION: 4
あなたの会社は3つのモバイルアプリケーションのスイートを作成しています。
アプリケーションビルドへのアクセスを制御する必要があります。ソリューションは組織レベルで管理する必要があります何を使用しますか?回答するには、回答領域で適切なオプションを選択します。
注:それぞれ正しい選択は1ポイントの価値があります。
Answer:
Explanation:
Explanation
Explanation:
Box 1: Microsoft Visual Studio App Center distribution Groups
Distribution Groups are used to control access to releases. A Distribution Group represents a set of users that can be managed jointly and can have common access to releases. Example of Distribution Groups can be teams of users, like the QA Team or External Beta Testers or can represent stages or rings of releases, such as Staging.
Box 2: Shared
Shared distribution groups are private or public distribution groups that are shared across multiple apps in a single organization. Shared distribution groups eliminate the need to replicate distribution groups across multiple apps.
Note: With the Deploy with App Center Task in Visual Studio Team Services, you can deploy your apps from Azure DevOps (formerly known as VSTS) to App Center. By deploying to App Center, you will be able to distribute your builds to your users.
References: https://docs.microsoft.com/en-us/appcenter/distribution/groups