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What is the duration, language, and format of Huawei Certified ICT Associate-Artificial Intelligence
- Number of Questions: 55
- Length of Examination: 160 minutes
- Passing score: 80%
- Format: Multiple choices, multiple answers
Understanding functional and technical aspects of Overview of Deep Learning
The following will be discussed in HUAWEI H13-311 exam dumps:
- Utilizing the Model
- TensorFlow Development Environment Setup
- Characterizing the TensorFlow Input Node
- Testing the Model
- Repeat and Update Parameters to the Optimal Solution
- TensorFlow Characteristics
- Instating All Variables
- Fundamental Development Steps Using TensorFlow
- Characterizing the Learning Parameter Variable
- TensorFlow Modules
- Upgrading Functions and Objectives
- TensorFlow Overview
- Characterizing the Operation
- TensorFlow Basics
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:
- Most extreme Likelihood Estimation
- Perceptron and Training Rules
- Enhancer
- Outline of Deep Learning
- Enactment Functions
- Uses of Deep Learning
- Propaedeutics of Deep Learning
- Regularization in Deep Learning
- Learning Algorithms
- Sorts of Neural Networks
- Bayes Estimation
- Boundary Estimation
- Definition and Development of Neural Networks
- Normal Machine Learning Algorithms
- Hyperparameter and Validation Set
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Huawei H13-311-ENU Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Computer Vision and NLP Basics | - Image recognition and processing - Natural language processing fundamentals |
| Topic 2: Huawei AI Development Platform and Tools | - MindSpore framework introduction - ModelArts platform basics |
| Topic 3: Data Processing and Feature Engineering | - Data cleaning and preprocessing - Feature extraction and selection |
| Topic 4: Machine Learning Basics | - Supervised and unsupervised learning - Common algorithms overview (regression, classification, clustering) |
| Topic 5: AI Application Practice | - End-to-end AI solution workflow - Basic model training and deployment concepts |
| Topic 6: Deep Learning Fundamentals | - CNN and RNN principles - Neural network basics |
| Topic 7: Artificial Intelligence Fundamentals | - AI application scenarios and industry overview - AI concepts and development history |






