Overview
Diffusion models are a type of generative model used in deep learning to create new data samples. They work by gradually adding noise to data and then learning how to reverse this process. This introduction will cover the basic concepts, how diffusion models differ from other generative models, and their applications in various fields like image and audio generation.
📚 Key Learning Objectives
- ✓ Explain the basic principles of diffusion models.
- ✓ Identify key differences between diffusion models and other generative models.
- ✓ Describe the process of adding and removing noise in diffusion models.
- ✓ Discuss real-world applications of diffusion models.
- ✓ Evaluate the strengths and limitations of diffusion models.
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