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NEW QUESTION: 1
ローカルペナルティ検出データのスケーリング戦略を実装する必要があります。
どの正規化タイプを使用する必要がありますか?
A. Streaming
B. Weight
C. Batch
D. Cosine
Answer: C
Explanation:
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
References:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Topic 1, One case study
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals:
* Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
* Access a user's tendency to respond to an advertisement.
* Customize styles of ads served on mobile devices.
* Use video to detect penalty events.
Current environment
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.
* The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
* Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.
* Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1 +/-5%.
* The ad propensity model uses cost factors shown in the following diagram:
220-1002-Deutsch Fragenkatalog
The ad propensity model uses proposed cost factors shown in the following diagram:
220-1002-Deutsch Fragenkatalog
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:
220-1002-Deutsch Fragenkatalog
Penalty detection and sentiment
Findings
* Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
* Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
* Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
* Notebooks must execute with the same code on new Spark instances to recode only the source of the data.
* Global penalty detection models must be trained by using dynamic runtime graph computation during training.
* Local penalty detection models must be written by using BrainScript.
* Experiments for local crowd sentiment models must combine local penalty detection data.
* Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
* All shared features for local models are continuous variables.
* Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
* Ad response rates declined.
* Drops were not consistent across ad styles.
* The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
* Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
* All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
* Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
* The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.

NEW QUESTION: 2
IT organizations have at their disposal software assets that, If systematically reused in the transformation to a Service-Oriented Architecture, can potentially offer huge savings each year by increasing development productivity and decreasing software maintenance costs. The measurement that estimates an asset's value based on the development hours avoided by reusing rather than recreating it, is called?
A. Predicted Costs Saved
B. Predicted Development Costs Saved
C. Predicted Asset Value Estimation
D. Predicted Reuse Savings
E. Predicted Net Hours Saved
Answer: E
Explanation:
Within the confines of this focused approach, an asset'sestimated value is based on thedevelopment costs avoided by reusing rather than recreating it. That measurement isreferred to here as Predicted Net Hours Saved. By reusing existing assets, anorganization avoids both the costs of repeatedly developing the same functionalityand the costs of maintaining different implementations of the same functionality.Reuse consolidates functionality and reduces redundancy. The more that reuse occurs,the greater the savings.
Reference:http://www.oracle.com/technetwork/topics/entarch/oracle-pg-soa-roi-from-reuser3-2-1561706.pdf

NEW QUESTION: 3
Which of the following techniques enables a highly secured organization to assess security weaknesses in real time?
A. Continuous monitoring
B. Access control lists
C. Baseline reporting
D. Video surveillance
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Continuous monitoring point toward the never-ending review of what resources a user actually accesses, which is critical for preventing insider threats. Because the process is never-ending, assessments happen in real time.

NEW QUESTION: 4
A reliable measure is one that is:
A. trusted to be practical and to produce accurate data.
B. consistent across observers and measurement occasions.
C. socially valid as indicated by caregiver reports.
D. effective as evidenced by peer-reviewed journal articles.
Answer: B


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