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The questions for H13-311_V3.5 were last updated on Apr 30, 2025.
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Sigmoid, tanh, and softsign activation functions cannot avoid vanishing gradient problems when the network is deep.
Activation functions like Sigmoid, tanh, and softsign suffer from the vanishing gradient problem when used in deep networks. This happens because, in these functions, gradients become very small as the input moves away from the origin (either positively or negatively). As a result, the weights of the earlier layers in the network receive very small updates, hindering the learning process in deep networks. This is one reason why activation functions like ReLU, which avoid this issue, are often preferred in deep learning.
"Today's speech processing technology can achieve a recognition accuracy of over 90% in any case." Which of the following is true about this statement?
While speech recognition technology has improved significantly, its accuracy can still be affected by external factors such as noise, background sound, accents, and speech clarity. Although systems can achieve over 90% accuracy under controlled conditions, the accuracy drops in noisy or complex real-world environments. Therefore, the statement that today's speech processing technology can always achieve high recognition accuracy is incorrect.
Speech recognition systems are sophisticated but still face challenges in environments with heavy noise, where the technology has difficulty interpreting speech accurately.
When learning the MindSpore framework, John learns how to use callbacks and wants to use it for AI model training. For which of the following scenarios can John use the callback?
In MindSpore, callbacks can be used in various scenarios such as:
Early stopping: To stop training when the performance plateaus or certain criteria are met.
Saving model parameters: To save checkpoints during or after training using the ModelCheckpoint callback.
Monitoring loss values: To keep track of loss values during training using LossMonitor, allowing interventions if necessary.
Adjusting the activation function is not a typical use case for callbacks, as activation functions are usually set during model definition.
Which of the following algorithms presents the most chaotic landscape on the loss surface?
Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it updates the model parameters for each individual training example, which can introduce a significant amount of noise into the optimization process. This leads to a less smooth and more chaotic path toward the global minimum compared to methods like batch gradient descent or mini-batch gradient descent, which provide more stable updates.
Which of the following are covered by Huawei Cloud EIHealth?
Huawei Cloud EIHealth is a comprehensive platform that offers AI-powered solutions across various healthcare-related fields such as:
Drug R&D: Accelerates drug discovery and development using AI.
Clinical research: Enhances research efficiency through AI data analysis.
Diagnosis and treatment: Provides AI-based diagnostic support and treatment recommendations.
Genome analysis: Uses AI to analyze genetic data for medical research and personalized medicine.
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