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Huawei HCIA-AI V3.5 Sample Questions (Q363-Q368):

NEW QUESTION # 363
Which of the following are common gradient descent methods?

A. Multi-dimensional gradient descent (MDGD)
B. Batch gradient descent (BGD)
C. Mini-batch gradient descent (MBGD)
D. Stochastic gradient descent (SGD)

Answer: B,C,D

Explanation:
The gradient descent method is a core optimization technique in machine learning, particularly for neural networks and deep learning models. The common gradient descent methods include:
Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from the entire dataset.
Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data, combining the benefits of both batch and stochastic gradient descent.
Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point, leading to faster but noisier updates.
Multi-dimensional gradient descent is not a recognized method in AI or machine learning.

NEW QUESTION # 364
Deflated AI The logical architecture of the processor does not include which of the following options?

A. Chip system control CPU
B. AI Calculation engine
C. DVPP
D. GPU

Answer: D

NEW QUESTION # 365
Which of the following does the convolutional neural network structure not include?

A. Pooling layer
B. Loop layer
C. Fully connected layer
D. Convolutional layer

Answer: B

NEW QUESTION # 366
The optimizer is an important part of training neural networks. The purpose of using the optimizer does not include which of the following:

A. Reduce the difficulty of manual parameter setting
B. Avoid local extremes
C. Speed up algorithm convergence
D. Avoid overfitting

Answer: D

NEW QUESTION # 367
TensorFlow 2.0 The way to view tensor dimensions in is?

A. device
B. dtype
C. dimens
D. ndim

Answer: D

NEW QUESTION # 368
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Test H13-311_V3.5 Duration & 100% Latest H13-311_V3.5 Official Cert Guide Library - HCIA-AI V3.5

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Huawei HCIA-AI V3.5 Sample Questions (Q363-Q368):

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