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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:
- Artificial intelligence Society in the Future
- The Past, Present, and Future of AI
- Advancement of AI Industries
- Key Planning of AI in the World
- Equity and Equity in the Era of AI
- Artificial intelligence Governance
- Human-Machine Relationship in the Era of AI
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Understanding functional and technical aspects of Overview of TensorFlow, programming basics, and methods of using TensorFlow programming for image recognition, speech recognition, and human-machine dialogue
The following will be discussed in HUAWEI H13-311 exam dumps:
- Hyperparameter and Validation Set
- Uses of Deep Learning
- Sorts of Neural Networks
- Boundary Estimation
- Learning Algorithms
- Enactment Functions
- Enhancer
- Propaedeutics of Deep Learning
- Perceptron and Training Rules
- Regularization in Deep Learning
- Bayes Estimation
- Definition and Development of Neural Networks
- Outline of Deep Learning
- Normal Machine Learning Algorithms
- Most extreme Likelihood Estimation
Huawei H13-311-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
| AI Development Framework | 20% | - AI Development Technologies
- 1. Python for AI
- 2. MindSpore Framework
- 3. Model Development Workflow
- 4. AI Model Deployment
|
| Machine Learning Overview | 20% | - Machine Learning Fundamentals
- 1. Unsupervised Learning
- 2. Common Machine Learning Algorithms
- 3. Supervised Learning
- 4. Model Training and Evaluation
|
| AI Overview | 15% | - Artificial Intelligence Fundamentals
- 1. AI Application Scenarios
- 2. AI Industry Trends
- 3. AI Concepts and Development History
|
| Cutting-edge AI Applications | 6% | - Emerging AI Technologies
- 1. Generative AI
- 2. AI Ethics and Governance
- 3. Large Language Models
|
| Introduction to Huawei AI Platforms | 14% | - Huawei AI Ecosystem
- 1. Huawei Cloud AI Services
- 2. Ascend AI Processors
- 3. ModelArts
- 4. Atlas Products
- 5. CANN
|
| Deep Learning Overview | 25% | - Deep Learning Fundamentals
- 1. Recurrent Neural Networks
- 2. Transformer Models
- 3. Convolutional Neural Networks
- 4. Neural Networks
|