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Introduction to Diffusion Models Diffusion models represent a prominent class of generative models that have garnered attention in the machine learning community for their unique approach to data generation. At their core, diffusion models rely on a process that gradually adds noise to data until it becomes indistinguishable from a noise distribution and subsequently learns

Introduction to Diffusion Models Diffusion models represent a critical advancement in the realm of generative modeling, capturing the complexities of data distributions through a unique probabilistic framework. These models function by gradually transforming a simple noise distribution into a more intricate target data distribution, essentially simulating a diffusion process that can yield high-quality synthetic data.

Introduction to Reinforcement Learning and its Planning Challenges Reinforcement Learning (RL) is a domain within artificial intelligence that emphasizes how agents should take actions in an environment to maximize cumulative rewards. At its core, RL involves several fundamental components: agents, environments, states, actions, and rewards. An agent interacts with its environment by making decisions, observing