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Huawei H13-311-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Mathematics and Python Basics | 15% | - Linear algebra, probability, statistics, calculus fundamentals - Python syntax, data structures, libraries: NumPy, Pandas, Matplotlib |
| Machine Learning Overview | 20% | - Common algorithms: regression, decision tree, SVM, clustering - Supervised, unsupervised, reinforcement learning - Model training, evaluation, overfitting, underfitting |
| Huawei AI Solutions & Applications | 5% | - Ascend chips, Atlas hardware, CANN - Full-stack all-scenario AI strategy - Edge/cloud AI deployment scenarios |
| AI Overview | 15% | - Definition, history and development trends of AI - Main technical branches: ML, DL, computer vision, NLP - Typical industry applications and AI ethics |
| AI Development Frameworks | 20% | - Huawei Cloud ModelArts, Ascend AI ecosystem - MindSpore framework fundamentals and operations - TensorFlow/PyTorch basics |
| Deep Learning Overview | 25% | - CNN, RNN/LSTM, Transformer basics - Activation, loss functions, optimizers - Neural network structure and backpropagation |
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