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Huawei HCIA-AI (Huawei Certified ICT Associate-Artificial Intelligence) - H13-311-ENU real prep

H13-311-ENU
  • Exam Code: H13-311-ENU
  • Exam Name: HCIA-AI (Huawei Certified ICT Associate-Artificial Intelligence)
  • Updated: Jul 14, 2025
  • Q & A: 175 Questions and Answers
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About Huawei H13-311-ENU: HCIA-AI (Huawei Certified ICT Associate-Artificial Intelligence)

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Who should take the Huawei Certified ICT Associate-Artificial Intelligence

Engineers who need to master AI technologies, learn and use deep learning algorithms, and master Huawei AI-related product technologies.

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:

  • Outline of Deep Learning
  • Enhancer
  • Sorts of Neural Networks
  • Normal Machine Learning Algorithms
  • Definition and Development of Neural Networks
  • Hyperparameter and Validation Set
  • Uses of Deep Learning
  • Learning Algorithms
  • Most extreme Likelihood Estimation
  • Perceptron and Training Rules
  • Regularization in Deep Learning
  • Enactment Functions
  • Propaedeutics of Deep Learning
  • Boundary Estimation
  • Bayes Estimation

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Understanding functional and technical aspects of Essential mathematics knowledge and pre-knowledge for Deep Learning

The following will be discussed in HUAWEI H13-311 exam dumps:

  • Organized Statistical Model
  • Likelihood Distribution
  • Flood and Underflow
  • Eigendecomposition
  • Moore-Penrose Pseudoinverse
  • Follow Operator
  • Numeric Calculation
  • Uncommon Matrices
  • Likelihood Theory and Information Theory
  • Model: Linear Least Squares
  • Sick Condition
  • Bayesian Rules
  • Model: Principal Component Analysis
  • Inclination Based Optimization Method
  • Ceaseless Variable
  • Irregular Variables
  • Data Theory
  • Determinants
  • Minor Probability
  • Particular Value Decomposition
  • Restrictive Probability
  • Assumption, Variance, and Covariance
  • Linear Algebra
  • Freedom and Conditional Independence
  • Basic Probability Distribution
  • Imperative Optimization

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