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What are the advantages of deep learning-based speech recognition algorithms?
A. No data training
B. Automated feature extraction
C. Forced alignment of annotated data
D. End-to-end task processing
Answer: B,D
Explanation:
Deep learning-based speech recognition offers two key advantages over traditional approaches:
* Automated feature extraction (B):Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.
* End-to-end task processing (C):Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.
Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.
Exact Extract from HCIP-AI EI Developer V2.5:
"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: End-to-End Speech Recognition
NEW QUESTION # 59
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
A. ReLU
B. Sigmoid
C. SeLU
D. Tanh
Answer: B
Explanation:
Thesigmoidactivation function maps inputs to the range (0, 1) and has a maximum derivative of 0.25 at x=0.
This derivative value is always # 0.5, making it the correct choice here. While sigmoid is historically used in neural networks, it suffers from the vanishing gradient problem for large positive or negative inputs due to its small derivative values. Other functions such as ReLU, Tanh, and SeLU have different derivative behaviors, with ReLU having a derivative of 1 for positive inputs, Tanh having derivatives up to 1, and SeLU designed for self-normalizing networks with derivatives potentially greater than 0.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Sigmoid compresses values into the (0,1) range, with its maximum derivative being 0.25, which is always less than 0.5." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Activation Functions in Neural Networks
NEW QUESTION # 60
Which of the following are required for the image object detection algorithm?
A. Confidence calculation
B. Object location calculation
C. Object classification determination
D. Object contour calculation
Answer: A,B,C
Explanation:
An object detection system must:
* Classifythe detected object (A).
* Locatethe object by generating bounding box coordinates (C).
* Estimate confidencescores indicating prediction reliability (D).
Object contour calculation (B) is a separate task often related toinstance segmentation, not general object detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Object detection includes classification, bounding box localization, and confidence score prediction.
Contour detection belongs to segmentation tasks."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Workflow
NEW QUESTION # 61
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
A. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
B. For CNN, there is no difference in handling text or image tasks.
C. CNNs are suitable for image tasks, but they perform poorly in text tasks.
D. Color image input is multi-channel, whereas text input is single-channel.
Answer: A,D
Explanation:
In CNN usage:
* A:True - color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.
* B:True - in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.
* C:False - there are clear differences in handling between text and image data.
* D:False - CNNs can perform very well in text classification when used appropriately.
Exact Extract from HCIP-AI EI Developer V2.5:
"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN in NLP
NEW QUESTION # 62
When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
In Python's scikit-learn library, this is implemented using: Answer:
Explanation:
python
CopyEdit
from sklearn.feature_selection import SelectKBest, chi2
SelectKBest selects the top K features based on scores returned by the chi2 function.
Exact Extract from HCIP-AI EI Developer V2.5:
"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Feature Selection Methods
NEW QUESTION # 63
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