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Huawei H13-324_V2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Neural Network Basics | 25% | - Model training, optimization and evaluation - Mathematical foundation for AI development - Common neural network structures and algorithms - Basic principles of deep learning |
| MindSpore Full-Process Development | 20% | - Model saving, loading and deployment - MindSpore framework features and installation - Automatic differentiation and parallel computing - Network construction, training and inference |
| CANN - Inference Application Development Process | 8% | - Multi-thread and batch processing design - Application development workflow - Data preprocessing and postprocessing - Inference engine and API introduction |
| CANN - Inference Application Performance Tuning | 5% | - Data transmission and computing efficiency improvement - Performance indicators and analysis methods - System configuration and resource optimization |
| Comprehensive Experiment and Application Practice | 5% | - End-to-end AI application development based on Ascend - Deployment and testing of actual projects - Problem analysis and solution design |
| CANN - ATC Model Conversion | 8% | - Model optimization and accuracy comparison - ATC tool usage and parameter configuration - Conversion from mainstream frameworks to Ascend OM model - CANN architecture and functions |
| Ascend AI Processor | 8% | - Memory management and data flow - Architecture and features of Ascend AI processor - Hardware specifications and computing resources |
| Atlas AI Computing Solution | 13% | - Atlas hardware product series and application scenarios - System composition and deployment modes - Software stack and development environment |
| CANN - Custom Operator Development | 8% | - Operator performance analysis and tuning - Operator development framework and specifications - Development, compilation and deployment of custom operators |
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