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When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.
A. FALSE
B. TRUE
Answer: B
Explanation:
In the HCIP-AI EI Developer V2.5 study guide, the loss function is defined as a core component in training deep neural network models. It serves as a quantitative measure of how well the model's predictions match the actual ground truth labels. By calculating the difference between predicted outputs and actual labels, the loss function provides feedback that the optimization algorithm (such as gradient descent) uses to update model parameters. This process is iterative, aiming to minimize the loss value, thereby improving prediction accuracy. For example, in classification tasks,Cross-Entropy Lossis commonly used, while in regression tasks,Mean Squared Error (MSE)is typical. The smaller the loss, the better the model's performance on the given data.
Exact Extract from HCIP-AI EI Developer V2.5:
"A loss function is an objective function that evaluates the difference between the model output and the real label. The goal of training is to minimize this loss so that the model predictions approach the actual values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training and Evaluation
NEW QUESTION # 17
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
A. FALSE
B. TRUE
Answer: B
Explanation:
A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation- Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models
NEW QUESTION # 18
Overfitting is a condition where a model is overly simple and excessive generalization errors occur.
A. TRUE
B. FALSE
Answer: B
Explanation:
Overfitting occurs when a model learns the training data too well, including its noise and outliers, to the extent that it negatively impacts performance on unseen data. Contrary to the statement, overfitting is not caused by an "overly simple" model but typically by an overlycomplex modelwith too many parameters relative to the amount of training data. Such models have high variance and low bias, meaning they fit the training data perfectly but fail to generalize to new datasets. In the HCIP-AI EI Developer V2.5 curriculum, overfitting is described as a scenario where the model's complexity captures random fluctuations in training data instead of general patterns, leading to poor predictive performance.
Exact Extract from HCIP-AI EI Developer V2.5:
"Overfitting means that the trained model performs very well on the training dataset but poorly on new data.
It usually results from excessive model complexity, insufficient data, or lack of regularization." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training Challenges
NEW QUESTION # 19
In cases where the bright and dark areas of an image are too extreme, which of the following techniques can be used to improve the image?
A. Grayscale compression
B. Gamma correction
C. Grayscale stretching
D. Inversion
Answer: B
Explanation:
When the contrast between bright and dark areas is extreme,gamma correctionis effective in adjusting luminance in a non-linear way to balance these extremes.
* If# < 1, dark areas are brightened, highlights are compressed.
* If# > 1, bright areas are emphasized, shadows are compressed.Other methods like grayscale stretching and compression target linear contrast changes, while inversion flips pixel values but doesn't balance extreme light/dark ranges effectively.
Exact Extract from HCIP-AI EI Developer V2.5:
"Gamma correction adjusts image brightness non-linearly, suitable for correcting overly bright or overly dark regions, improving overall visibility." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Enhancement
NEW QUESTION # 20
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 # 21
......
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