Exam Name: HCIP-AI-Application Developer V1.0
Certification Provider: Huawei
Corresponding Certification: Huawei-certification
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Instant Download Huawei : H13-325_V1.0 Questions & Answers as PDF & Test Engine
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Huawei H13-325_V1.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Neural Network Basics | 4% | - Deep feedforward networks - Backpropagation algorithms - Neural network architecture design - Artificial neural networks - Deep learning fundamentals |
| Topic 2: ModelArts AI Development Platform | 4% | - Data processing and management - Model development, training, and deployment - ModelArts architecture and functions |
| Topic 3: Huawei AI Development Strategy and Portfolio | 2% | - Full-stack, all-scenario AI solutions - Huawei Cloud EI services overview |
| Topic 4: Image Processing Lab Practices | 12% | - End-to-end image application development - CNN model development on ModelArts - Image processing with Python and OpenCV |
| Topic 5: Integrated AI Application Development | 12% | - End-to-end AI project design - ModelArts-based full lifecycle development - Application integration and optimization |
| Topic 6: Natural Language Processing Theory and Applications | 10% | - NLP fundamentals - Text preprocessing and feature engineering - Word embedding and language models - Text classification, sentiment analysis, NER - Sequence models for NLP |
| Topic 7: Image Processing Theory and Applications | 26% | - Basic image processing tasks - Image preprocessing techniques - Convolutional Neural Networks (CNN) - Image classification, detection, segmentation - Feature extraction and traditional algorithms - Digital image processing basics - Computer vision overview |
| Topic 8: NLP Lab Practices | 10% | - NLP model building and training - Text processing and analysis - NLP application development |
| Topic 9: Speech Processing Lab Practices | 10% | - Speech application deployment - Speech model training and optimization - Speech feature extraction |
| Topic 10: Speech Processing Theory and Applications | 10% | - RNN, LSTM, and advanced speech models - Speech processing overview - Deep neural network models for speech - Traditional speech models (GMM, HMM) - Speech recognition and synthesis applications |
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