# diffusion

**Repository Path**: jingma-git/diffusion

## Basic Information

- **Project Name**: diffusion
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-18
- **Last Updated**: 2023-10-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# [How Diffusion Models Work](https://www.deeplearning.ai/short-courses/how-diffusion-models-work/)
In How Diffusion Models Work, you will gain a deep familiarity with the diffusion process and the models which carry it out. More than simply pulling in a pre-built model or using an API, this course will teach you to build a diffusion model from scratch.

In this course you will:

- Explore the cutting-edge world of diffusion-based generative AI and create your own diffusion model from scratch.
- Gain deep familiarity with the diffusion process and the models driving it, going beyond pre-built models and APIs.
- Acquire practical coding skills by working through labs on sampling, training diffusion models, building neural networks for noise prediction, and adding context for personalized image generation.
- At the end of the course, you will have a model that can serve as a starting point for your own exploration of diffusion models for your applications.

This one-hour course, taught by Sharon Zhou will expand your generative AI capabilities to include building, training, and optimizing diffusion models.

Hands-on examples make the concepts easy to understand and build upon. Built-in Jupyter notebooks allow you to seamlessly experiment with the code and labs presented in the course.