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Huawei H13-321_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Neural Network Basics | 4% | - Activation functions and regularization - Gradient descent and backpropagation - Basic concepts of neural networks - Multilayer Perceptron (MLP) |
| Image Processing Theory and Applications | 26% | - OCR and visual application development - Convolutional Neural Networks (CNN) - Image processing fundamentals - Image classification, object detection, segmentation |
| Natural Language Processing Theory and Applications | 10% | - Word representation and embedding - BERT, GPT and pre-trained models - RNN, LSTM, GRU, Transformer architecture - Text classification, NER, machine translation |
| Natural Language Processing Lab Guide | 10% | - Application integration and deployment - Text classification and NER implementation - ModelArts NLP model training and tuning |
| Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Full-stack and all-scenario AI technology layout - Huawei AI development strategy |
| Image Processing Lab Guide | 12% | - ModelArts-based image classification - Ascend-based deployment - Object detection and segmentation practice |
| Overview of ModelArts | 4% | - Data processing, training, deployment capabilities - ModelArts platform positioning and architecture - Development environment and tool usage |
| Speech Processing Lab Guide | 12% | - ASR and TTS service development - ModelArts speech application deployment - Huawei Cloud Speech Interaction Service |
| Speech Processing Theory and Applications | 10% | - Automatic Speech Recognition (ASR) - Acoustic and language modeling - Speech signal characteristics and processing - Text-to-Speech (TTS) technology |
Huawei HCIP-AI-EI Developer V2.0 Sample Questions:
1. Which of the following is not an advantage of the ReLU series of activation functions over the Sigmoid and Tanh activation functions?
A) The unilateral inhibition of ReLU provides the sparse expression ability of the network.
B) From a computational perspective, both Sigmoid and Tanh activation functions require exponential calculations and are highly complex, while ReLU only requires a threshold to obtain an activation value.
C) ReLU can cause neuron death during its training process.
D) The non-saturation of ReLU can effectively solve the problem of gradient disappearance and provide a relatively wide activation boundary.
2. Language model can be used to dynamically generate word vectors for each word.
A) True
B) False
3. Which of the following is correct about the Gaussian distribution curve?
A) The Gaussian curve is bell-shaped, with high ends and low in the middle.
B) The Gaussian curve is bell-shaped, with low ends and high in the middle.
C) The larger the standard deviation, the taller the curve.
D) The smaller the standard deviation, the flatter the curve
4. What color does the RGB value (255, 0, 0) represent?
A) Pure black
B) Pure green
C) Pure blue
D) Pure red
5. The processing effect of bilinear interpolation is smoother than that of nearest neighbor interpolation.
A) True
B) False
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: A |
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