Tag Archives: Learning Note

Deep Learning 22: Diffusion Models (2)

Previously, we introduced Autoencoders and Hierarchical Variational Autoencoders (HVAEs). In this post, we will cover the details of Denoising Diffusion Probabilistic Models (DDPM). Diffusion Models We can treat DDPM as a restricted HVAE. Here, each only depends on . In DDPM, we do not have parameters to add noises, and it is a predefined GaussianContinue reading “Deep Learning 22: Diffusion Models (2)”

Reinforcement Learning (1): Q-Learning basics

Hi! In the following posts, I will introduce Q-Learning, the first part to learn if you want to pick up reinforcement learning. But before that, let us shed light on some fundamental concepts in reinforcement learning (RL). Kindergarten Example Q-Learning works in this way: do an action and get reward and observation from the environment,Continue reading “Reinforcement Learning (1): Q-Learning basics”