Learning Transformer Through Implementation

This course will help you become a deep learning-based computer vision expert needed in the field through in-depth theoretical explanations of Object Detection and Segmentation, along with practical examples that can be immediately applied in real-world work environments.

This course is designed to help you learn the core functions of FastAPI, as well as the entire process of web service development. Through this course, we will help you become a FastAPI expert developer needed in the field.

From the core of Kafka to in-depth content on internal mechanisms, the course is structured so that even beginners can quickly reach the expert level through detailed theoretical explanations, hands-on practice, and practical Kafka application development practice.

It provides a key guide to understanding the internal mechanisms of Oracle DB architecture and growing as a performance tuning and performance analysis expert.

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 :)

By implementing various practical data analysis cases using SQL, you can simultaneously improve your data analysis and SQL utilization skills.

We will help you easily understand the core concepts of machine learning and acquire the ability to implement practical machine learning applications by moving away from theory-based machine learning courses.

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

This course is designed to help you learn the use of ksqlDB and its core mechanisms through various hands-on exercises. After completing the course, you will be able to easily and quickly build a real-time streaming data analysis system based on Kafka.

Through in-depth theoretical explanations of Kafka Connect and detailed practical training that can be used immediately in the field, we will help you grow into an expert in building data linkage and data pipelines based on Kafka Connect that are needed in the field.

If you want to be recognized as a machine learning expert based on large-scale data, from understanding the core framework of Spark machine learning, to SQL-based data processing through difficult practical problems, to data analysis through business domain analysis, and to the ability to implement optimized machine learning models, please join this course.

From core theories of Deep Learning and CNN to implementation methods of various CNN models, and practical Deep Learning development know-how through real-world problems, If you want to become a Deep Learning CNN technology expert, join us in this lecture :)

This course is designed to upgrade your skills as a practical machine learning development expert by implementing the machine learning model of the Home Credit Default Risk competition on Kaggle.

This is a course covering the GitHub notebooks and bonus content from <Build a Large Language Model from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Build a Large Language Model from Scratch> is the Korean translation of the bestseller <Build a Large Language Model (from Scratch)> (Manning, 2024) by Sebastian Raschka. This book provides a way to learn and utilize the operating principles of large language models by building a complete model starting from scratch with OpenAI's GPT-2 model.

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 covers basic natural language processing techniques and various text tasks using deep learning.

Natural Language Processing (NLP) is one of the fastest growing areas of artificial intelligence. This course covers a wide range of topics, from the basics of NLP to the latest NLP techniques using deep learning. In particular, it provides an in-depth understanding of cutting-edge generative models such as ChatGPT.

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.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

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.

"Your first step to creating a customized LLM with LoRA-based lightweight fine-tuning!" This is an introductory hands-on course designed so that even those new to LLMs can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading the model → applying data → training → comparing results. In a short time, you'll directly experience the workflow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, and gain an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

JavaScript, which is known to all web developers, now explore the world of machine learning with this powerful language! This course learns how to build and deploy machine learning models using JavaScript and the powerful machine learning library Tensorflow.js. It guides you step-by-step through all the technologies required to develop web-based machine learning applications. Through this course, learners will systematically understand the core principles of machine learning. In addition, you will learn how to develop deep learning models using JavaScript and Tensorflow.js APIs, how to utilize transfer learning based on pre-trained models, and how to apply all this knowledge interactively in a browser environment.

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 :)

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

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 is a machine learning lecture written in easy-to-understand language, like a story, without any formulas—tailored perfectly for those who are humanities students to the core!

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

This course starts with the question "If I had been on the Titanic, could I have survived?" and completes a full-stack project that develops an AI model to predict survival probability based on actual data and serves it as a web service. You'll practice the entire process of AI and web development, from deep learning modeling using PyTorch, building a backend server with FastAPI, to implementing a user interface with Next.js.

An introductory AI lecture tailored for those new to artificial intelligence! It easily summarizes core concepts from data to generative AI. The goal is not to build AI, but to achieve a level of practical understanding where you can ask the right questions and explain the technology.