최신Huawei HCIP-AI-EI Developer V2.5 - H13-321_V2.5무료샘플문제
문제1
Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
정답: A
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문제2
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
정답: D
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문제3
Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)
Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)
정답:
Decoder
Explanation:
The Seq2Seq architecture is widely used in machine translation, speech recognition, and other NLP tasks. It consists of:
* Encoder:Processes the input sequence and encodes it into a fixed-length context vector containing semantic information.
* Decoder:Uses this context vector to generate the target output sequence step by step.
Exact Extract from HCIP-AI EI Developer V2.5:
"Seq2Seq models consist of an encoder and a decoder. The encoder transforms the input into a context vector, which the decoder uses to generate the output sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Architecture
Explanation:
The Seq2Seq architecture is widely used in machine translation, speech recognition, and other NLP tasks. It consists of:
* Encoder:Processes the input sequence and encodes it into a fixed-length context vector containing semantic information.
* Decoder:Uses this context vector to generate the target output sequence step by step.
Exact Extract from HCIP-AI EI Developer V2.5:
"Seq2Seq models consist of an encoder and a decoder. The encoder transforms the input into a context vector, which the decoder uses to generate the output sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Architecture
문제4
The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.
The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.
정답: B
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문제5
The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
정답: A
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문제6
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
정답: A
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문제7
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
정답: A
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