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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
- Bayes Estimation
- Sorts of Neural Networks
- Learning Algorithms
- Outline of Deep Learning
- Enhancer
- Most extreme Likelihood Estimation
- Uses of Deep Learning
- Normal Machine Learning Algorithms
- Propaedeutics of Deep Learning
- Definition and Development of Neural Networks
- Boundary Estimation
- Enactment Functions
- Perceptron and Training Rules
- Regularization in 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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Huawei H13-311-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Huawei AI Platform & Applications | 8% | - Typical AI application scenarios - Huawei Cloud EI and ModelArts - Ascend computing platform and Atlas series |
| Topic 2: AI Development Frameworks | 12% | - TensorFlow basics and usage - Huawei MindSpore framework introduction |
| Topic 3: Deep Learning Overview | 25% | - Neural network fundamentals - CNN, RNN and basic architectures - Training mechanisms and optimization |
| Topic 4: Machine Learning Overview | 20% | - Model training and evaluation - Common algorithms and models - Basic concepts and classification |
| Topic 5: Mathematics and Python Basics | 20% | - Linear algebra, probability and statistics - Data processing and visualization - Python programming fundamentals |
| Topic 6: Artificial Intelligence Overview | 15% | - AI technology and application fields - History and development of AI - Huawei AI strategy and full-stack architecture |







