Understanding functional and technical aspects of Overview of Deep Learning
The following will be discussed in HUAWEI H13-311 exam dumps:
- Upgrading Functions and Objectives
- Characterizing the Learning Parameter Variable
- TensorFlow Overview
- TensorFlow Basics
- TensorFlow Development Environment Setup
- TensorFlow Characteristics
- Instating All Variables
- Characterizing the TensorFlow Input Node
- Testing the Model
- Repeat and Update Parameters to the Optimal Solution
- Fundamental Development Steps Using TensorFlow
- Utilizing the Model
- Characterizing the Operation
- TensorFlow Modules
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Understanding functional and technical aspects of Overview of artificial intelligence
The following will be discussed in HUAWEI H13-311 exam dumps:
- Advancement of AI Industries
- The Past, Present, and Future of AI
- Key Planning of AI in the World
- Artificial intelligence Governance
- Equity and Equity in the Era of AI
- Human-Machine Relationship in the Era of AI
- Artificial intelligence Society in the Future
What is the duration, language, and format of Huawei Certified ICT Associate-Artificial Intelligence
- Format: Multiple choices, multiple answers
- Passing score: 80%
- Length of Examination: 160 minutes
- Number of Questions: 55
Huawei H13-311-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Overview | 15% | - Main technical branches: ML, DL, computer vision, NLP - Definition, history and development trends of AI - Typical industry applications and AI ethics |
| Deep Learning Overview | 25% | - CNN, RNN/LSTM, Transformer basics - Neural network structure and backpropagation - Activation, loss functions, optimizers |
| Mathematics and Python Basics | 15% | - Linear algebra, probability, statistics, calculus fundamentals - Python syntax, data structures, libraries: NumPy, Pandas, Matplotlib |
| AI Development Frameworks | 20% | - Huawei Cloud ModelArts, Ascend AI ecosystem - MindSpore framework fundamentals and operations - TensorFlow/PyTorch basics |
| Huawei AI Solutions & Applications | 5% | - Ascend chips, Atlas hardware, CANN - Edge/cloud AI deployment scenarios - Full-stack all-scenario AI strategy |
| Machine Learning Overview | 20% | - Model training, evaluation, overfitting, underfitting - Common algorithms: regression, decision tree, SVM, clustering - Supervised, unsupervised, reinforcement learning |
