Core Principles of Generative AI Learned through ChatGPT and DALL-E

A New Framework for Human-AI Collaboration to Amplify Human Thinking How should AI be utilized to maximize the amplification of human capabilities? On this new stage of creativity, what criteria should govern human-AI collaboration? Furthermore, how should jobs and workflows be designed? This course presents a new framework and execution strategies to answer these questions.

Innovation in Work Efficiency and Creativity – Agentic AI AI is now evolving beyond simple automation into a new stage where it can judge and execute on its own. Agentic AI is at the center of this change, serving as a core technology that helps businesses and organizations operate smarter. In this course, we have organized the content clearly and simply so you can understand how Agentic AI connects decision-making with execution and how it can be applied to practical work. Discover the essential knowledge for every professional preparing for the future right now.

AI Finally Gets a Body - What is My Survival Strategy in the Age of Robots? Physical AI is not just about robots. It is an overthrow of the economic paradigm created by the grand convergence of artificial intelligence, robotics, and energy technology. This course explores various perspectives, from the background of Physical AI's emergence to the strategies of global big tech, hardware sovereignty, and future social issues such as robot taxes, basic income, and global inequality.

The rules of every game are changing — a new dimension of business opened by quantum computing. Moving beyond simple technological advancement, quantum computing has already emerged as a key variable determining the future business landscape, poised to shift paradigms across all industries. This analysis provides insightful perspectives on the massive wave of technological change, covering everything from the basic principles of quantum computers to innovation cases in finance, pharmaceuticals, security, and the strategic moves of global Big Tech. It offers the most definitive roadmap for professionals who wish to gain quantum literacy without complex physics knowledge and transform into future-ready talent.

Reading the Standard of Digital Money — A New Financial Order Opened by Stablecoins In the uncertain digital asset market, stablecoins—the only assets that function "like real money"—have already emerged as a core part of the global financial infrastructure. This provides essential, condensed knowledge for professionals who want to be at the forefront of understanding the paradigm shift in the digital economy.

Understand the operating principles of deep learning-based generative AI models and acquire utilization methods through hands-on practice.

A course to learn deep learning paper implementation skills, implementing the GPT-1 (Improving Language Understanding by Generative Pre-Training) paper from scratch with TensorFlow 2.0.

This explains the foundational principles of large language models like ChatGPT with a focus on theory.

We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.

This course is designed to help beginners understand artificial intelligence in a simple, practical, and structured way. Many students feel overwhelmed by AI because of complex terminology, unclear explanations, or a lack of real examples. In this course, I focus on breaking down AI concepts into easy-to-understand ideas, using visual tools, real-world examples, and step-by-step demonstrations. Instead of heavy mathematics or theory, you will learn how AI works, how to use popular AI tools, and how to apply AI confidently for learning, productivity, development, and business use cases. By the end of the course, you will have a solid foundation in AI, understand modern AI tools, and feel comfortable exploring more advanced topics.

NO CODING NEEDED. MOST COMPREHENSIVE AI AGENTS & AI AUTOMATION COURSE FOR ALL SKILL LEVELS USING N8N. WE GO OVER HOW TO BUILD AI AGENTS & AI AUTOMATIONS EITHER FOR YOURSELF OR FOR YOUR CLIENTS.WE HELP BEGINNERS AUTOMATE ANYTHING WITH AI. AI is advancing at a rapid rate, don't get left behind. Learn how to implement AI in your personal life or for your business. This is a once-in-a-lifetime opportunity to get ahead of AI and learn how to build useful AI Agents and AI Automations to take your skills to the next level. We cover how to use AI beyond ChatGPT, and build real, working AI Agents and automations. This Complete AI Agents Course is the most complete AI Agents & AI Automation course on the market, with over 7 hours of course content covering AI Agent basics, an n8n tutorial, building AI Agents & Automations for businesses, trading stocks and crypto using AI Agents, sports betting with AI Agents and getting new business ideas from AI Agents. What You'll Learn in This Course: AI Agents & AI Automations Basics: We cover what AI Agents are and when to use them Complete n8n Tutorial : Learn how to use n8n, a no-code tool for AI Agents & AI Automation Building AI Agents for Businesses - Learn how to build real, working AI Agents for businesses in any industry Building AI Agents for Trading (Stocks & Crypto): Learn how to build AI Agents for trading crypto, stocks and sports betting Real-World Application: We go over real world applications and use cases when building AI Agents, for both professional and personal use cases. By the end of this complete course, you'll have the knowledge to build real, working AI Agents and AI Automations either for your own business or for your clients.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

You use ChatGPT, but haven't you found it difficult to explain why it gives certain answers? "I know terms like RAG, Agents, and Fine-tuning... but it's hard to explain them accurately." "I get tongue-tied whenever I hear LLM-related terminology." "Conceptual explanations are always vague during AI meetings." This course was created specifically for people like you. This is a theoretical course designed to help you understand LLMs not just as a 'tool,' but as a 'structure.' It doesn't teach you how to use ChatGPT or Gemini; instead, it establishes the foundation so you can explain exactly why they work the way they do.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

Get a clear overview of exam types and trends at a glance! Sharpen your practical skills with a total of 120 questions. The perfect preparation for the AWS AI Practitioner exam, designed for systematic learning even when studying alone.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

This course is designed to learn the fundamental theories and practical processes necessary for applying AI modeling and service planning to real-world work environments. Learners can acquire essential foundational knowledge to skillfully perform AI modeling projects and experience various practical procedures used in actual business settings. The course is specifically structured to provide step-by-step learning of the entire workflow from AI service environment analysis → goal setting → requirements analysis → service model design → scenario planning → utilization planning → execution plan development → performance evaluation planning. Through this approach, learners can develop systematic thinking and practical application skills for how to introduce and utilize AI from business and service planning perspectives, rather than limiting themselves to purely technical learning. Upon completing this course, learners will understand the overall planning and execution process of AI projects and will be able to strengthen their capabilities as AI service planners, PMs, and data-driven strategists in actual corporate environments.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

This course aims to cover the latest Gemini API SDK (@google/genai) in a Node.js and TypeScript environment. You will learn how to use multimodal technology to process text, images, and video simultaneously. We will practice the entire process, from issuing an API key to project setup and model calling.

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python