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DeepLearning.AI 2

This is the title of an AI course

  • up to 1 hour
  • Beginner

In this course, you will gain a deep familiarity with the diffusion process and the models which carry it out. You will learn to build a diffusion model from scratch, implement algorithms to speed up sampling, and ac quire practical coding skills through hands-on labs. 1

  • Diffusion models
  • Generative AI
  • Neural networks
  • Noise prediction
  • Image generation

Overview

In How Diffusion Models Work, 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.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    Spanish
    course language
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

AI Enthusiasts 1

Individuals interested in understanding and building diffusion models from scratch.

Intermediate Python Programmers

Programmers with knowledge of Python, Tensorflow or Pytorch who are looking to expand their skills in generative AI.

Data Scientists

Professionals looking to enhance their capabilities in generative AI and implement diffusion models in their projects.

Why should you take this course?

This course will teach you to build, train, and optimize diffusion models, expanding your generative AI capabilities. Ideal for AI enthusiasts, intermediate Python programmers, and data scientists, it provides practical coding skills and hands-on labs to help you achieve your goals....

Pre-Requisites

1 / 2

  • Knowledge of Python

  • Familiarity with Tensorflow or Pytorch

What will you learn?

Introduction to Diffusion Models
Understand the basics of diffusion models and their applications in generative AI.
Building a Diffusion Model
Learn to create a diffusion model from scratch, including the necessary coding and algorithms.
Training Diffusion Models
Acquire skills to train diffusion models effectively, optimizing their performance.
Sampling Algorithms
Implement algorithms to speed up sampling by 10x, enhancing the efficiency of your models.
Neural Networks for Noise Prediction
Build neural networks specifically designed for noise prediction in diffusion models.
Personalized Image Generation
Add context to your models for personalized image generation, tailoring outputs to specific needs.
Hands-on Labs
Engage in practical coding exercises using built-in Jupyter notebooks to experiment with the concepts learned.

Meet your instructor

  • Sharon Zhou

    Cofounder & CEO, Lamini

    Sharon Zhou is a cofounder and CEO of Lamini, an LLM startup based on her PhD dissertation in generative AI and her experience as a product manager. She is also a CS Faculty member at Stanford and was named to MIT Technology Review's 35 Under 35.

Upcoming cohorts

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