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- Assumption, Variance, and Covariance
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- Particular Value Decomposition
- Ceaseless Variable
- Bayesian Rules
- Organized Statistical Model
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- Outline of computerized reasoning
- Outline of TensorFlow, programming nuts and bolts, and techniques for utilizing TensorFlow programming for picture acknowledgment, discourse acknowledgment, and human-machine exchange
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- Outline of Deep Learning
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The following will be discussed in HUAWEI H13-311 exam dumps:
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- Testing the Model
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- TensorFlow Overview
- Characterizing the TensorFlow Input Node
Huawei H13-311-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Development Frameworks | 20% | - TensorFlow/PyTorch basics - Huawei Cloud ModelArts, Ascend AI ecosystem - MindSpore framework fundamentals and operations |
| Topic 2: AI Overview | 15% | - Main technical branches: ML, DL, computer vision, NLP - Typical industry applications and AI ethics - Definition, history and development trends of AI |
| Topic 3: Deep Learning Overview | 25% | - CNN, RNN/LSTM, Transformer basics - Activation, loss functions, optimizers - Neural network structure and backpropagation |
| Topic 4: Mathematics and Python Basics | 15% | - Python syntax, data structures, libraries: NumPy, Pandas, Matplotlib - Linear algebra, probability, statistics, calculus fundamentals |
| Topic 5: Huawei AI Solutions & Applications | 5% | - Ascend chips, Atlas hardware, CANN - Edge/cloud AI deployment scenarios - Full-stack all-scenario AI strategy |
| Topic 6: Machine Learning Overview | 20% | - Supervised, unsupervised, reinforcement learning - Model training, evaluation, overfitting, underfitting - Common algorithms: regression, decision tree, SVM, clustering |
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