LDM 부터 DiT 까지, 구현하며 배우는 Diffusion 완전정복 II

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.

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.

From the basics of deep learning natural language processing to the latest models such as Transformer and BERT, learn the principles and utilization methods of deep learning natural language processing (NLP) through various examples and practical code implementations.

Hot image generation model! Let's understand it by implementing it from the basics! Let's implement AUTO ENCODER / VAE / GAN / DIFFUSION together!

From Multi Head Attention to the Original Transformer model, BERT, and Encoder-Decoder based MarianMT translation model, you'll learn Transformer inside and out by implementing them directly with code.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

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

If you invest 3 hours a day for 6 days, you can learn the basics of deep learning using PyTorch. Let's get started!

This is a course that intensively covers gradient descent, the most fundamental learning algorithm in deep learning.

AI is not a technology that suddenly appeared. To properly see AI's present and future, you must understand 70 years of challenges and failures. 👉 "The moment you understand AI history, you can see both its principles and future together." Start right now!

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students can directly use the GPT API to create chatbots and run them on the web using Streamlit. By experiencing everything from basics to hands-on practice, you can solidly establish your first steps in AI utilization.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

Is AI difficult because of complex algorithms? Actually, the real power of AI comes from 'data'. You need to understand the essence of data to properly handle AI. This is a lecture that explains the roots of AI from a data perspective, covering everything from basic philosophy to perceptrons, CNNs, and data processing and ethics.

The ultimate machine learning and deep learning tutorial that teaches you like a 1:1 private lesson with Hanbit Media's Honkong series, understand with hundreds of hand drawings, and practice right away with just a browser through Google Colab

Explaining how to connect to a Python-based AI web server using FastAPI from a Spring Boot project.

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

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

If you have a vague curiosity about generative AI but don't know where to start, this course is the perfect answer. It's structured to explain the core principles of AI technology clearly and concisely, allowing anyone to quickly grasp the essence. We'll go beyond simply knowing 'what it is' to deeply explore why it works that way and how to utilize it effectively. This course will serve as the most reliable starting point to help you confidently and proactively leverage technology in the rapidly changing AI era.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

Learn how to build a real-time fire detection system using Next.js, YOLOv11, and FastAPI.