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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:
- Human-Machine Relationship in the Era of AI
- Advancement of AI Industries
- Key Planning of AI in the World
- The Past, Present, and Future of AI
- Artificial intelligence Society in the Future
- Artificial intelligence Governance
- Equity and Equity in the Era of AI
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: AI Development Frameworks | 20% | - MindSpore framework fundamentals and operations - Huawei Cloud ModelArts, Ascend AI ecosystem - TensorFlow/PyTorch basics |
| Topic 2: Huawei AI Solutions & Applications | 5% | - Edge/cloud AI deployment scenarios - Ascend chips, Atlas hardware, CANN - Full-stack all-scenario AI strategy |
| Topic 3: Machine Learning Overview | 20% | - Model training, evaluation, overfitting, underfitting - Common algorithms: regression, decision tree, SVM, clustering - Supervised, unsupervised, reinforcement learning |
| Topic 4: AI Overview | 15% | - Typical industry applications and AI ethics - Definition, history and development trends of AI - Main technical branches: ML, DL, computer vision, NLP |
| Topic 5: Mathematics and Python Basics | 15% | - Python syntax, data structures, libraries: NumPy, Pandas, Matplotlib - Linear algebra, probability, statistics, calculus fundamentals |
| Topic 6: Deep Learning Overview | 25% | - CNN, RNN/LSTM, Transformer basics - Neural network structure and backpropagation - Activation, loss functions, optimizers |
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