Introduction to Python with an AI Coding Assistant

: "From Mathematical Foundations to the Latest Models, Completing the Deep Learning Pipeline with PyTorch (44 Lectures in Total)" The era of simply learning how to call model.fit() is over. From the very basics of artificial neural networks—differentiation and gradient descent—to essential PyTorch techniques used in the industry, and CNN/RNN for handling image and time-series data! We will help you systematically master the entire process of deep learning. Go beyond analyzed data and step into the amazing world of deep learning, where artificial intelligence mimicking the human brain learns and makes decisions on its own.

We understand why Attention was needed and how it works by 'implementing it directly with code'. This lecture starts from the structural limitations of RNN and Seq2Seq models, experimentally verifies the information bottleneck problem and long-term dependency issues created by fixed context vectors, and naturally explains how Attention emerged to solve these limitations. Rather than simply introducing concepts, we directly confirm RNN's structural limitations and Seq2Seq's information bottleneck problems through experiments, and implement **Bahdanau Attention (additive attention)** and **Luong Attention (dot-product attention)** one by one to clearly understand their differences. Each attention mechanism forms Query–Key–Value relationships in what way, has what mathematical and intuitive differences in the weight calculation process, and why it inevitably led to later models naturally connects to their characteristics and evolutionary flow. We learn how Attention views sentences and words, and how each word receives importance weighting to integrate information in a form where formula → intuition → code → experiment are connected as one. This lecture is a process of building 'foundational strength' to properly understand Transformers, helping you deeply understand why the concept of Attention was revolutionary, and why all subsequent state-of-the-art NLP models (Transformer, BERT, GPT, etc.) adopt Attention as a core component. This lecture is optimized for learners who want to embody the flow from RNN → Seq2Seq → Attention not through concepts but through code and experiments.

Even those who have never experienced even the basics of coding can create automation software with just a few clicks. I'm serious.

Deep learning is a technology that learns data through neural networks composed of combinations of complex functions. In this lecture, we will mathematically understand the core concepts of deep learning and analyze them from the perspective of matrix operations. In particular, utilizing Python's NumPy library, we will visually examine how parameters are updated by directly implementing the forward and backward propagation processes of deep learning. Even the seemingly complex neural network structure becomes clear when analyzed with matrix operations. This lecture focuses more on understanding concepts than coding and is suitable for students who wish to intuitively grasp the principles of deep learning mathematically.

This is an introductory course for those using Claude Code for the first time. You won’t do the coding yourself—Claude writes both the code and the configuration files. Starting with creating a psychological test in the browser, you’ll build a household budget using card transaction records, create an escape room game, and make a travel itinerary that connects to external services like Google Calendar and Notion. Since you learn each feature as it becomes necessary, you’ll also understand why that feature is needed.

"Why does my automated trading system collapse in a falling market?" I, too, spent a great deal of time grappling with the same question. Most automated trading strategies on the market may appear to generate profits during bull markets, but the moment market conditions change, they lose their ability to adapt. To solve this problem, I combined the expertise I developed through two years of hands-on research with the latest AI technology. In this course, you will learn how to design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and the movements of individual assets, and how to automatically train and deploy it in a free GPU environment. In particular, by introducing sophisticated data normalization techniques on par with those used by hedge funds, you will build an 'intelligent comprehensive scoring system' that goes beyond simple indicators to incorporate news sentiment. On top of that, an ‘AI safety gate’ infused with the insight of a trader with 20 years of Wall Street experience adds a final layer of conviction from the perspective of a human trader just before a purchase. Going beyond simple buying and selling, you will implement the entire process of an institutional-grade algorithmic system yourself—including dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reports.

The online course “AI Teammates Do It All” is a hands-on program that teaches you how to use AI not merely as an automation tool, but as an “AI teammate” that works alongside you on practical tasks. The course is structured around common real-world workflows, including market research, proposal writing, strategy report creation, brand content visualization, project promotion and operations, and follow-up management. Participants assign various AI tools—including ChatGPT, Perplexity, Copilot, Canva, Gamma, MiriCanvas, and Vrew—as planning partners, strategic research partners, design partners, and operations support partners, and complete actual work deliverables with them. Across 30 sessions, you will learn step by step, from the basic mindset for working with AI to practical prompt writing, market research, proposal and report writing, brand content creation, and project operations. By following the course, you can develop the AI collaboration skills to work on your own like an entire team.

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

From coding basics to launching your own app all at once?! The App Build All-in-One, connecting fundamentals and practical application.

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.

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!

Have you wanted to implement RAG but felt overwhelmed by connecting everything from embeddings to vector storage and search? Based on real-world service development experience, this course explains the core concepts of pgvector and the service source code. By calling the API directly, you can quickly understand the entire flow of how RAG vector search works.

Core Principles of n8n Automation and Workflow Design Basics for Non-Majors 5-Step Automation Built with n8n: API Connection Practice from Topic Input to Video Completion How to Automatically Create Videos in n8n Using AI APIs (ChatGPT, ElevenLabs, Creatomate)

The real story of an AI-native company run by GolbinHacker, the 1st place winner of Kakao AI TOP 100 and author of the bestseller "The Best Prompt Engineering Lecture." It explores how just two developers build and operate a service, along with the new competencies developers and organizations need in the AI era through real-world cases.

With the advancement of generative AI, many companies are pursuing AX (AI Transformation). However, in the actual field, there are not many educational programs that systematically teach what to do and how to do it compared to the demand that "AX must be done." While most AI education focuses on how to use generative AI or write prompts, this course focuses on the "AX Strategy Planning Methodology" for transforming corporate tasks and services into AI-based ones. This course is based on the practical experience of a 25-year expert who has performed DX/AX consulting and training for large corporations, the financial sector, public institutions, and various companies in the actual IT field. It allows you to learn from a practical perspective not just how to use AI well, but which tasks should undergo AX, to what extent AI should make decisions, how to control risks, and how to design and verify AX projects. Furthermore, it systematically covers core content necessary for actual AX implementation, from understanding the essence of AX to AI thinking systems, AX design methodology, Human-AI role separation, Guardrail design, and KPI and verification structures. In particular, it goes beyond simple theoretical education. Based on real corporate cases such as insurance underwriting, customer consultation, manufacturing quality, internal work support, marketing recommendations, development productivity, and management decision-making, it provides guide templates and sample materials for AX problem definitions, AX hypothesis definitions, AX performance designs, AX service structure diagrams, Guardrail designs, and AX verification plans. This ensures that after taking the course, you can secure the practical capabilities to easily plan and promote actual AX tasks. The biggest differentiator of this lecture is that it teaches how to plan and design AX from the perspective of someone who actually performs AX projects. Specifically, by providing AX strategy planning output templates and seven industry-specific AX cases that can be used in the field, it is designed so that students can refer to cases most similar to their own work and apply them immediately by modifying them to fit their environment. In short, this is not a course to create AI users, but a process to create AX strategy planners who can design and execute a company's AX.

How long will you keep struggling with repetitive daily research and document writing? I, too, was an ordinary office worker who worked overtime every day trying to find key insights amidst an overflow of information. However, after integrating Gemini 3 into my workflow, I experienced a significant increase in work speed. Boost your productivity by utilizing Gemini in your tasks.

We solve the challenge of knowing Python syntax but struggling to build services through hands-on experience at Naver and step-by-step practice.

For those who have tried running hand-recognition examples but feel lost when it comes to teaching an AI your own hand gestures and turning them into something useful, I’ll guide you all the way from installation to data collection, training, and completing the web interface using just a webcam, drawing on my experience overseeing development and training vocational instructors.

In the era of generative AI, it is difficult to solve practical problems simply by writing prompts. In real-world services, you need to be able to design AI systems that can make decisions autonomously, call tools, retrieve data, and collaborate across multiple agents. This course is a hands-on program for everyone from developers designing AI agents for the first time to engineers seeking to build systems that can be operated in production. You will learn step by step, from designing agent architectures with LangGraph to State Management, Tool Calling, API and MCP integration, natural-language database control, RAG implementation, semantic search, Few-shot Learning, Reflection, and Fine-tuning. You will also learn how to build multi-agent collaboration structures, use the Supervisor pattern and Actor-Critic strategies, implement asynchronous communication, create automated evaluation systems, and establish Quality Gates through real-world projects. Finally, you will learn the MLOps and AI Agent operations strategies required in real production environments, including monitoring based on Metrics, Logs, and Tracing; Shadow Deployment; Canary Deployment; Drift Detection; RCA automation; and A/B testing. By the end of the course, you will be able to directly design, develop, and operate high-performance AI systems that can be applied to real-world services using the latest AI agent technologies.

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.