AI for Everyone: First Steps in Deep Learning Through Mathematics

Systematically learn the core concepts and operating principles of Imitation Learning and VLA (Vision-Language-Action) technologies, which serve as the foundation for Physical AI. Understand the mechanisms through which Physical AI interacts with the real world through actual case study analysis.

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.

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.

🔥Don’t have time to complete the 32-hour all-in-one course? We’ve condensed the four subjects of the Big Data Analysis Engineer written exam into around 4 hours. Connect concepts directly to actual exam questions through modified past exam questions, and receive 150 pages of lecture materials plus 13 pages of final review notes for the exam room.

Feeling overwhelmed by where to start with AI auditing? AI has moved beyond a mere technology trend and is rapidly expanding into corporate decision-making, workflow automation, customer service, recruitment, security, and risk management. However, compared to the speed of AI adoption, not many people systematically understand questions like: "Is the AI being properly controlled?", "Can we trust the AI's judgment?", or "What risks could arise from the data, models, and operational processes?" In particular, those working in IT auditing, security, internal control, and project management often face these concerns: - How does AI auditing differ from traditional IT auditing? - Is it enough to just look at the code for an AI model, or must we examine the data and training process as well? - How do AI governance, risk, bias, explainability, MLOps, and incident response connect to one another? - Where should I start to understand the concepts covered in AI auditing certification courses like AAIA from a practical perspective? This course was created specifically to resolve this sense of uncertainty. For about 20 years, I have gained experience in IT service planning, development, PM, collaboration platform operation, groupware construction, chatbot service planning, and security and audit-related tasks. I have firsthand experience in how systems are planned, developed, and operated in actual organizations, and how failures, changes, security, and user requirements are interconnected. Furthermore, I have synthesized the perspectives of IT auditing and AI auditing through CISA and AAIA study programs. In this course, rather than diving deep into complex AI technology through math or development, I focus on the core concepts that auditors and IT practitioners must understand. We will examine AI systems by breaking them down into the flow of data, models, operations, security, governance, risk, and audit procedures, and summarize what controls and checkpoints are required at each stage. Through this course, students will understand that AI auditing is not simply "evaluating AI," but rather the task of inspecting governance, risk, and control systems to ensure that AI is used safely and responsibly within an organization. This course is suitable for: - Those new to AI auditing. - Those in IT auditing or security who want to expand into the AI domain. - Those who want to organize AI governance and risk from a practical perspective. - Those who want to grasp the overall concepts before starting AI audit-related studies such as AAIA. Upon completing the course, you will be able to answer the following questions yourself: - What components make up an AI system? - What should an AI auditor check during the data, model, and operation stages? - Why are AI governance and Responsible AI important? - How are AI risk, bias, explainability, privacy, and security connected? - What perspectives and evidence should be included in an AI audit report? In the rapidly changing era of AI, the ability to manage and inspect technology to make it trustworthy is just as important as the ability to use it well. This course is an introductory program for those who want to understand the big picture of AI auditing step-by-step from the beginning. Let's build the first standard for looking at IT and AI from an auditing perspective together.

Customer analysis completed with Excel and Orange, from cross-selling to A/B testing and churn risk management all at once

You can benchmark manufacturing innovation methods for semiconductors and run machine learning and deep learning with semiconductor data without coding.

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 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.

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.

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.

We provide an easy understanding of the concepts and principles of generative AI from a humanities perspective, including literature, film, music, and history, while exploring the cultural impacts and life changes AI will bring. We offer practical insights and strategies on how individuals and companies should respond and prepare for the AI era.

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

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

A machine learning course explained in simple, story-like language—with no equations—at a level even a die-hard humanities major can understand!

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

Building a Full-Stack System Without Coding: Complete the entire pipeline from data collection to AI/ML analysis and web dashboards using only Korean prompts (Vibe Coding). Owning a 100% Automated Pipeline: Secure an independent stock analysis tool that sends S&P 500 screening and investment opinions via Telegram every morning. Acquiring a Practical Data-Based Investment Framework: Master institutional-level data analysis and risk management capabilities based on source code currently in actual operation.

What if AI could directly open a browser and write shell scripts to resolve server issues? Through practical automation know-how implemented with OpenClaw across Windows and Linux, we guide you on the shortest path to completing your own powerful remote development environment without complex configurations.

Aim for a quick pass of the first nationally certified AI qualification! Includes three sets of practical problem-solving exercises reconstructed by the instructor after taking the exam personally, along with code templates.

This course covers the entire process of evolving ChatGPT from a simple conversational AI into an intelligent AI Agent that performs real-world tasks. By utilizing the three core frameworks—LangChain, LangGraph, and LangSmith—you will build AI Agents that can be used immediately in practice.