Do you know the basics but feel overwhelmed when it comes to applying them in practice? Drawing on real-world experience, we make complex LLM architectures easy to understand.

A general education lecture for the public to shed the vague fear of artificial intelligence. Through concrete examples, we learn what AI learning truly means. Also, via specific examples, we examine AI's 'brain structure' and how it thinks.

Many people use ChatGPT and frequently hear about recommendation systems and autonomous driving, but when asked to explain “why they work the way they do,” they often struggle to find the words. If you only follow coding courses, the code runs but the underlying principles don’t stick. If you only take math courses, you see the formulas but can’t connect them to AI. Overseas courses can feel intimidating because of the English, while introductory courses in Korea tend to focus heavily on hands-on practice. While building real models in AI competitions, running Everyone’s AI, and meeting thousands of learners, I repeatedly saw the same thing: the obstacle wasn’t how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation became disconnected from one another. This course bridges that gap. From how functions define inputs and outputs, to how derivatives determine the direction of learning, and how backpropagation passes errors backward, it explains everything in one continuous flow—from the fundamentals of mathematics to neural networks and softmax. Rather than memorizing formulas or simply copying code, this course helps you understand “why” through equations and intuitive illustrations. For anyone who wants to truly understand AI, it will serve as a solid first step toward moving on to deep learning.

An AI that understands Korean well, handles long documents, and even carries out real-world tasks—that is exactly the direction Solar Open 2 is aiming for. This class provides an accessible explanation of Upstage’s Solar Open 2 technical report in the order of “Introduction → Design → Training → Evaluation.” Technical terms are explained through everyday analogies and visual materials, enabling even beginners to understand “what,” “how,” and “why” as one coherent flow.

Learn the core concepts and operating principles of Physical AI, which moves beyond the screen to be implemented in robots, autonomous vehicles, and drones. Understand the sensing-perception-action mechanism, digital twin-based learning, and the importance of embodied data, while exploring the global technological competition and Korea's strategic direction through real-world cases in industries such as manufacturing, mobility, and agriculture.

Mastering difficult AI with just one single analogy?! A first step into AI principles, understanding through core concepts rather than just terminology.

As AI technology advances rapidly, it is becoming increasingly important to understand how AI works, learns, and makes decisions—not just how to use AI tools. This comprehensive course is designed to provide a structured introduction to the fundamentals and operating principles of AI, from data training and decision-making to prediction, AI service development, and problem-solving. Using a range of examples and analogies, the course explains how AI learns from and processes data, how it generates results and makes decisions, and how AI services are developed and structured as complete systems, making these concepts accessible even to beginners. The course also explores the challenges and limitations that can arise when applying AI in practice, and introduces basic approaches to analyzing and addressing them. By the end, learners will have a comprehensive understanding of the full process, from how AI works to how services are developed and problems are solved. They will also build a structured way of thinking about AI and a foundation for applying it in practice.

You will learn about Transformers inside out by directly implementing everything from Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder-based MarianMT translation model, and the Vision Transformer in code.

You can learn about Text-to-SQL systems that generate and query SQL statements in real time in response to user questions.

Do you want to make substantial profits even in a bear market? Are you interested in automated trading but unable to get started because of coding, trading strategy design, exchange APIs, and server deployment? In this course, even those new to Python will learn how to use ChatGPT and Codex to turn trading ideas into concrete strategies and implement them through backtesting and live automated trading code. You will practice Binance futures automated trading using Python and CCXT, technical indicator strategies, averaging-down backtests, and a Kimchi Premium strategy based on the Upbit API. You will then apply Telegram notifications and order safety features, manage the completed bot with GitHub, and deploy it to a cloud server. Rather than entrusting all investment decisions to an LLM, you will determine the strategy and risk management criteria yourself, using the LLM as an assistant for strategy design, code writing, and troubleshooting. After completing the course, you will be able to do more than simply run the provided code—you will be able to create, validate, modify, and deploy your own strategies!

You can transform your ideas into beautiful presentations in just a few minutes. No more spending hours struggling with slide design. With GAMMA's AI technology, you can dramatically improve your content creation process.

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will gain a broad range of knowledge, from establishing AI-based system testing strategies to designing and executing test cases, as well as utilizing AI for test support. This course is based on the ISTQB AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

Complete Understanding of Robotics High- and Low-Level Controllers, Sensors, and Ultra-High-Speed Communication Concepts Through the Unitree Go2 Example

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!

Learn the core principles and practical implementation of Classic RAG, Graph RAG, and Agentic RAG. Design a dynamic routing system that optimizes token efficiency and latency, and build a long-term memory system that combines GraphRAG’s relational network reasoning with Agentic RAG’s self-evaluation loop. This course develops advanced RAG architecture design skills that can be applied directly in real-world settings.

We transform distribution reports that merely list numbers into a single page that supports decision-making. Drawing on 24 years of experience in distribution operations and headquarters, we use Excel and AI to analyze changes in sales and contribution margin and examine hypotheses about their causes. By calculating simulated distribution data ourselves, we complete performance reports containing key conclusions, execution conditions, and approval requests.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment