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 to reverse this process to generate new, similar data. This introduction will help you understand the basic concepts and applications of diffusion models in various fields, such as image and audio generation.
📚 Key Learning Objectives
- ✓ Define diffusion models and their purpose in deep learning.
- ✓ Explain the process of adding and removing noise in data.
- ✓ Identify real-world applications of diffusion models.
- ✓ Compare diffusion models with other generative models.
- ✓ Implement a simple diffusion model using a deep learning framework.
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