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:

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

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How to Prepare For Huawei Certified ICT Associate-Artificial Intelligence

Preparation Guide for Huawei Certified ICT Associate-Artificial Intelligence

Introduction for Huawei Certified ICT Associate-Artificial Intelligence

Passing the HCIA-AI V1.0 affirmation will demonstrate that you have dominated the AI advancement history, the Huawei Ascend AI framework, the full-stack all-situation AI strategy，and the calculations identified with customary AI and profound learning. You can fabricate, train, and convey neural organizations by utilizing advancement systems TensorFlow and MindSpore. You are capable for deals, advertising, item supervisor, project the executives, specialized help, and other AI positions. This test manages the forefront AI advances and improvement patterns. The science, Python, TensorFlow, and profound learning hypotheses and advancement rehearses needed by AI. The picture acknowledgment, discourse acknowledgment, and machine interpretation improvement measures.

The HCIA-AI V1.0 test covers: Overview of computerized reasoning, Python programming nuts and bolts, Essential arithmetic information and pre-information for Deep Learning, Overview of Deep Learning, Overview of TensorFlow, programming rudiments, and strategies for utilizing TensorFlow programming for picture acknowledgment, discourse acknowledgment, and human-machine exchange, Overview of Huawei cloud EI.

As a global leading ICT solution provider, Huawei advocates for an open, shared, ICT talent ecosystem that benefits all parties. In 2013, Huawei launched its ICT Academy, a school-enterprise cooperation project that involves higher education institutions, to help build that talent ecosystem. Over the past six years, Huawei has invested heavily in exploring practices with universities and colleges and replicating successful experiences. Huawei has built a talent supply chain covering the entire process of learning, certification, and employment - by deepening the cooperation mechanism between universities and enterprises, aiming to promote industry development, and innovate talent development models based on enterprise requirements. Huawei helps universities cultivate ICT talent that meets industry requirements, providing high-quality talent for industry development.

As digitalization accelerates in various industries, colleges and universities urgently need to reform traditional teaching content and focus more on cutting-edge technologies, ensure that courses keep up with the times, guarantee that teaching materials are related to industry practices, and help students become more employable. To meet these demands, Huawei works with college and university teachers to jointly develop courses based on Huawei's understanding of the industry, technical accumulation, and practices, industry position analysis, and Huawei-certified talent certification standard system, and builds a practical course system that adapts to industry talent requirement.

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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 Essential mathematics knowledge and pre-knowledge for Deep Learning

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

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

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Huawei H13-311-ENU Exam Syllabus Topics: