okjeong105171

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.

sotaaz

DDPM to DDIM, Complete Mastery of Diffusion Through Implementation I

실습 중심

생성형ai

트랜스포머

Stable Diffusion

Sotaaz

입문

초급

중급이상

파이썬

Python

Deep Learning(DL)

Course Intro

Understanding DDPM Principles Through Implementation

Learning DDIM Principles Through Implementation