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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deep Feedforward Networks | 5% | - Training and optimization methods - Neural network fundamentals - Network structure and activation functions |
| Topic 2: HiAI Service Development | 10% | - Pre-trained service calling - Application deployment and testing - End-cloud collaborative development |
| Topic 3: HiAI Foundation Development | 15% | - Model conversion and optimization - Inference engine usage - Hardware acceleration interface |
| Topic 4: HiAI Platform Overview | 10% | - Development environment setup - Architecture and positioning - Technical advantages and ecosystem |
| Topic 5: Computer Vision Theory and Applications | 25% | - CV model deployment on HiAI - Image processing basics - Feature extraction and classification |
| Topic 6: HiAI Engine Development | 15% | - Custom model integration - Performance tuning - Built-in AI engine APIs |
| Topic 7: Natural Language Understanding Theory and Applications | 8% | - Text preprocessing - Semantic representation models - NLU service integration |
| Topic 8: Automatic Speech Recognition Theory and Applications | 12% | - ASR application development - Speech signal processing - Acoustic and language models |
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