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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Image Processing Lab Guide | 12% | - ModelArts-based image classification - Ascend-based deployment - Object detection and segmentation practice |
| Topic 2: Natural Language Processing Theory and Applications | 10% | - BERT, GPT and pre-trained models - RNN, LSTM, GRU, Transformer architecture - Word representation and embedding - Text classification, NER, machine translation |
| Topic 3: Overview of ModelArts | 4% | - Development environment and tool usage - Data processing, training, deployment capabilities - ModelArts platform positioning and architecture |
| Topic 4: Neural Network Basics | 4% | - Activation functions and regularization - Gradient descent and backpropagation - Multilayer Perceptron (MLP) - Basic concepts of neural networks |
| Topic 5: Huawei AI Strategy and Full-Stack, All-Scenario AI Portfolio | 2% | - Huawei AI development strategy - Full-stack and all-scenario AI technology layout |
| Topic 6: Speech Processing Lab Guide | 12% | - Huawei Cloud Speech Interaction Service - ASR and TTS service development - ModelArts speech application deployment |
| Topic 7: Image Processing Theory and Applications | 26% | - Image classification, object detection, segmentation - Convolutional Neural Networks (CNN) - OCR and visual application development - Image processing fundamentals |
| Topic 8: Natural Language Processing Lab Guide | 10% | - Application integration and deployment - ModelArts NLP model training and tuning - Text classification and NER implementation |
| Topic 9: Speech Processing Theory and Applications | 10% | - Text-to-Speech (TTS) technology - Speech signal characteristics and processing - Automatic Speech Recognition (ASR) - Acoustic and language modeling |
Question 1
Which of the following is the highlights of Huawei's deep learning framework MindSpore?
A. Through MindSpore 's own technological innovation and the collaborative optimization of MindSpore and Ascend processors, efficient running state is achieved and computing performance is greatly improved.
B. By implementing the Al algorithm as code, the development environment becomes more friendly, significantly reducing model development time and lowering the model development threshold.
C. All of the above
D. Natively adapt to every scenario including terminal, edge and cloud, and can collaborate on demand, with flexible deployment.
Question 2
Which of the following are the request methods of REST API? (Multiple choice)
A. DELET
B. POST
C. GET
D. PATCH
E. PUT
Question 3
Machine learning is to automatically find patterns from data, but we need to divide the entire data set into a training set and a test set. The training set is used for training, and the test set is used to evaluate the generalization ability of the model.
A. True
B. False
Question 4
Which of the following processing flows belong to image preprocessing? (Multiple choice)
A. Object Detection
B. Histogram Normalization
C. Motion Tracking
D. Geometric Correction
Question 5
What problems of mean filtering can Gaussian filtering overcome?
A. Fuzzification
B. Large amount of calculation (same as C)
C. Slow calculation (the calculation principle is the same, so the calculation speed depends on the data size)
D. Denoising (the main function of Gaussian filtering is noise reduction)
Solutions:
| Question 1 Answer: C | Question 2 Answer: A,B,C,D,E | Question 3 Answer: A | Question 4 Answer: B,D | Question 5 Answer: A |
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