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