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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Huawei AI Development Strategy and AI Portfolio | - Huawei AI Ecosystem
|
| Topic 2: Neural Network Basics | - Neural Network Fundamentals
|
| Topic 3: ModelArts Platform Overview | - AI Development Platform
|
| Topic 4: Natural Language Processing Labs | - Practical Development
|
| Topic 5: Natural Language Processing Theory and Applications | - NLP Fundamentals
|
| Topic 6: Speech Processing Labs | - Practical Development
|
| Topic 7: Speech Processing Theory and Applications | - Speech AI Technologies
|
| Topic 8: Image Processing Theory and Applications | - Computer Vision Fundamentals
|
| Topic 9: Image Processing Labs | - Practical Development
|
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. Which of the following statements about tanh is correct? (Multiple choice)
A) Tanh also has the same disadvantage as the Sigmoid function, that is, the position derivative away from the center tends to 0
B) Tanh function can be seen as a logistic function that amplifies the translation
C) Tanh is symmetric about point 0, and the mean of the output is closer to 0 than Sigmoid, so SGD will be closer to the natural gradient, thereby reducing the number of iterations required
D) Tanh value range is (0, 1)
2. In the grayscale change experiment of image preprocessing, the gamma change function is defined as follows:
Xxxxxxxxxx
Def gamma_trans (img, gamma)
Gamma_list = [ np.power (x/255.0,gamma)*255.0 for x in range(256)]
Gamma_table = np.round ( np.array ( gamma_list )). astype (np.uint8)
Return cv2.LUT(img , gamma_table )
When the gamma value is 0.5, the bright part is stretched and the dark part is compressed.
A) False
B) True
3. Which of the following statements about the sigmoid function is correct? (Multiple choice)
A) Generally speaking, within 5 layers of Sigmoid network, the gradient will degenerate to 0, making it difficult to train. This phenomenon is called the vanishing gradient phenomenon.
B) Sigmoid function is monotonous and continuous, easy to derive, bounded output, and the network is easy to converge.
C) When the network is very deep, the modulus of the gradient becomes smaller and smaller
D) Sigmoid output is not centered on 0
4. The core problems of computer vision mainly include three categories: image recognition, () and image reconstruction.
5. After the image tag service is called, only one corresponding tag value is returned.
A) False
B) True
Solutions:
| Question # 1 Answer: A,B,C | Question # 2 Answer: A | Question # 3 Answer: A,B,C | Question # 4 Answer: Only visible for members | Question # 5 Answer: A |

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