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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Theoretical Knowledge and Applications of Natural Language Processing | 10% | - Language model and semantic understanding - Practical application - Machine translation, text generation and other technologies - Text processing and representation |
| Topic 2: Speech Processing Lab Guide | 12% | - Speech data processing practice - Application deployment and verification - Speech model building and tuning |
| Topic 3: Neural Network Basics | 4% | - Common neural network structures - Training and optimization methods - Basic concepts of neural networks |
| Topic 4: Overview of ModelArts | 4% | - Core functions and service modules - Basic operation process - ModelArts positioning and architecture |
| Topic 5: Theoretical Knowledge and Applications of Image Processing | 26% | - Feature extraction and representation - Image preprocessing technology - Typical application scenarios - Image classification, detection and segmentation |
| Topic 6: Theoretical Knowledge and Applications of Speech Processing | 10% | - Application cases - Speech recognition and synthesis - Speech feature extraction - Speech signal processing foundation |
| Topic 7: Image Processing Lab Guide | 12% | - Performance optimization and testing - Development environment setup - Image processing model development and deployment |
| Topic 8: Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development layout - Full-stack AI technology system - All-scenario AI solutions |
| Topic 9: Natural Language Processing Lab Guide | 10% | - Text preprocessing and feature engineering - End-to-end application development - NLP model training and evaluation |
1. Which of the following statements about the functions of the encoder and decoder is true?
A) The encoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
B) The encoder converts context vectors into variable-length output sequences.
C) The output lengths of the encoder and decoder are the same.
D) The decoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
2. The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
3. If OpenCV is used to read an image and save it to variable "img" during image preprocessing, (h, w) = img.
shape[:2] can be used to obtain the image size.
A) FALSE
B) TRUE
4. The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.
A) FALSE
B) TRUE
5. Which of the following are object detection algorithms?
A) YOLO
B) SSD
C) R-CNN
D) Faster-R-CNN
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: Only visible for members | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: A,B,C,D |
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