This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

You’ll learn Skills

1. Image Generation Techniques: You’ll learn various methods for generating images, including procedural generation, generative adversarial networks (GANs), autoencoders, and other deep learning techniques. You’ll understand the principles behind these methods and when to use them.
2. Computer Graphics Fundamentals: Courses often cover the basics of computer graphics, including concepts like pixel manipulation, color theory, and rendering. Understanding these fundamentals is essential for creating and editing images.
3. Deep Learning: If the course includes deep learning-based image generation techniques like GANs, you’ll learn about neural networks, backpropagation, optimization algorithms, and how to train and fine-tune models for image generation tasks.
4. Data Preprocessing: You’ll gain skills in preparing and cleaning image data for use in image generation models. This may involve resizing, normalization, augmentation, and data transformation techniques.
5. Tools and Frameworks: You’ll likely work with popular image manipulation and generation tools and frameworks, such as TensorFlow, PyTorch, Keras, or specialized libraries for computer graphics.
6. Image Editing Software: Courses may include instruction on using image editing software like Adobe Photoshop, GIMP, or similar tools to enhance and modify generated images manually.
7. Artistic and Creative Skills: Depending on the course’s focus, you may learn how to apply artistic principles, styles, and aesthetics to your generated images, making them visually appealing and creative.
8. Evaluation Metrics: Understanding how to evaluate the quality of generated images is important. You might learn about metrics like SSIM (Structural Similarity Index), PSNR (Peak Signal-to-Noise Ratio), and FID (Fréchet Inception Distance).
9. Ethical Considerations: Courses may discuss the ethical implications of image generation, especially in the context of deepfakes and misinformation, and how to use these skills responsibly.
10. Project-Based Learning: Many courses include practical projects where you’ll apply your skills to create images, artwork, or even interactive media. This hands-on experience is crucial for skill development.
11. Problem Solving: You’ll develop problem-solving skills related to image generation tasks, including debugging code, optimizing models, and troubleshooting issues with generated images.
12. Portfolio Development: By the end of the course, you should have a portfolio of generated images and projects that showcase your skills, which can be useful when seeking employment or freelance opportunities.

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