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Applied Deep Learning with PyTorch (Zero to Mastery)
This course provides a comprehensive introduction to Deep Learning using PyTorch, the most popular framework for machine learning research. Starting from tensor fundamentals, students will progress through the complete ML workflow, computer vision, modular software engineering, transfer learning, and model deployment. The curriculum is "code-first," emphasizing hands-on implementation and experimentation.
Introduction to Deep Learning
Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data using artificial neural networks. The course covers fundamental principles, underlying mathematics, optimization concepts (gradient descent, backpropagation), network modules (linear, convolution, pooling layers), and common architectures (CNNs, RNNs). Applications demonstrated include computer vision, natural language processing, and reinforcement learning. Students will use the PyTorch deep learning library for implementation and complete a final project on a real-world scenario.
AI Magic Lab
A rigorous course structure integrating four major sections: AI Fundamentals, Large Model Generation (GenAI & LLM), Agents and Evolutionary Computation (highlighted as a PolyU Feature), and Ethics. The course logic progresses sequentially through Perception & Data (L1-3), Cognition & Generation (L4-6), Agents & Evolution (L7-9), and concludes with Ethics & Future (L10).
AI Mastery Bootcamp: From Zero to Agent Architect
A 5-session intensive bootcamp designed to transform beginners into AI Agent Architects. The curriculum covers the 'BRIC' framework for prompt engineering, content acceleration for reading and viral copywriting, workplace automation for Excel and PowerPoint, building a 'Second Brain' using RAG (Retrieval-Augmented Generation), and creating autonomous digital employees.
Prompt Engineering Advanced Guide
A comprehensive advanced guide to mastering AI through structured logic and precise instruction. The course covers structural frameworks (CO-STAR), Few-Shot learning, Chain of Thought reasoning, output format constraints (JSON/Markdown), and prompt system management to resolve issues such as AI hallucinations and poor logical output.
OpenClaw: Architecture, Dev & Security for Local AI Agents
This course provides an in-depth analysis of OpenClaw, a groundbreaking open-source framework for autonomous AI agents. It systematically deconstructs the framework's layered system architecture, local-first RAG memory mechanisms, browser automation protocols, and highly scalable skill ecosystem. The curriculum covers practical orchestration of complex workflows, including PIV automation flows and multi-agent committee patterns. Furthermore, it critically analyzes hardware trade-offs in production-grade deployment paradigms and presents defense-in-depth strategies against core security threats such as RCE vulnerabilities and prompt injection. The course aims to empower senior developers and architects to build AI agent systems that possess high autonomy while remaining secure and controllable.
LLMs for Everyone: From Basics to Practical Use (2026 Edition)
This course is a beginner-friendly, practical introduction to Large Language Models (LLMs) such as ChatGPT and Gemini. Designed for learners from any background, it explains how LLMs work at a high level, what they can and cannot do, and how to use them effectively in study, work, and everyday life. Through hands-on demonstrations and guided exercises, you will learn prompt techniques, how to evaluate outputs critically, how to handle hallucinations and bias, and how to use common tools (e.g., documents, summaries, translation, data tasks) safely and responsibly. By the end of the course, you will be able to build a personal “LLM workflow” for real tasks—writing, research, planning, and productivity—without needing advanced coding skills.
【人教版】高中化学 选必3
本书为普通高中化学选择性必修教材，系统介绍了有机化学的基础知识。内容涵盖有机化合物的结构特点、研究方法、烃及其衍生物的性质与转化、生物大分子（糖类、蛋白质、核酸）以及合成高分子材料等核心领域，旨在培养学生的化学学科核心素养。
PolyU | Artificial Intelligence Concepts
This comprehensive course provides a rigorous yet accessible introduction to Artificial Intelligence, designed for postgraduate students and professionals. Bridging the gap between historical foundations and cutting-edge innovations, the curriculum progresses from symbolic AI and search algorithms to modern Deep Learning and Generative AI. Students will explore essential topics such as knowledge representation, probabilistic reasoning, and classical machine learning before diving deep into neural networks, Transformers, and Large Language Models (LLMs). Emphasizing both theory and practice, the course utilizes Python and industry-standard frameworks like PyTorch to implement algorithms, interact with modern APIs, and address critical issues in AI ethics and safety.
PolyU | Data Structures and Algorithms
Learn the fundamentals of data structures and algorithms, including arrays, linked lists, trees, sorting, and searching techniques.
Visual English Grammar - Welcome
Learn the basics of English grammar, vocabulary, and conversation skills.
Visual English Grammar - The Basics
Learn the basics of English grammar, vocabulary, and conversation skills.
Visual English Grammar - Intermediate Level
Learn the basics of English grammar, vocabulary, and conversation skills.
Visual English Grammar - Advanced Level
Learn the basics of English grammar, vocabulary, and conversation skills.
Visual English Grammar - English Tenses Mastery
Learn the basics of English grammar, vocabulary, and conversation skills.
Lighthouse for Hong Kong Integrated Practice: Book 9
A comprehensive English language practice workbook designed for Hong Kong students, focusing on reading comprehension, thematic vocabulary expansion, and practical grammar applications across five core units.
【人教版】初中英语 七年级上册
本教材为2024年秋季启用的新版七年级英语教材。全书包含三个过渡单元（Starter Units）和七个正式单元，内容涵盖自我介绍、家庭成员、校园环境、学科喜好、学校社团、日常生活及生日庆祝等主题。旨在通过Section A和Section B的多维度活动，夯实学生的语言基础，培养跨文化交际能力。
【人教版】初中英语 七年级下册
本书为人民教育出版社出版的七年级下册英语教材，依据2022年版课程标准编写。全书包含八个单元，涵盖动物、规则、健康、饮食、生活、天气、经历和故事等主题，通过听说读写及语音、语法、词汇的综合训练，全面培养学生的英语核心素养。
Math Readiness
This is a five-lesson early childhood math curriculum designed by an AI Tutor to help children transition from rote memorization to true mathematical logic. Through engaging, play-based activities, the course guides young learners across five progressive modules. It begins by building a strong foundation in number sense and quantity perception, then develops spatial awareness by exploring 2D and 3D geometric shapes. Children also cultivate their observation and early algebraic thinking through logic and pattern classification games. Additionally, the curriculum introduces practical measurement skills using everyday objects and concludes by making abstract time concepts concrete to help establish daily routines and planning.
Maths in Action (Primary 1-3)
This Primary 1 to Primary 3 Mathematics Curriculum is designed to build a solid and comprehensive mathematical foundation for early learners. The syllabus is systematically structured across five core strands: Number, Measures, Shape and Space, Data Handling, and Further Learning. Throughout this stage, students will progress from basic number recognition and arithmetic operations to developing spatial awareness, mastering practical measurement skills, and learning introductory data visualization. Beyond theoretical knowledge, the curriculum emphasizes cultivating logical thinking and problem-solving abilities, encouraging students to apply abstract mathematical concepts to real-world scenarios.