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Google Cloud Training

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.

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What's inside

Syllabus

Introduction to Image Generation
This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. 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.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Builds a strong foundation of image generation and diffusion models for beginners
Helps learners understand the theory behind diffusion models and how to train and deploy them
Taught by Google Cloud Training, recognized for their expertise in cloud computing and AI
Leverages Google Cloud's state-of-the-art image generation models and tools
Requires learners to come in with some background knowledge in machine learning

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Image Generation with these activities:
Review core concepts on diffusion
Begin by reviewing the fundamentals of diffusion, including Fick's Laws and their implications for mass transfer. This will provide a strong foundation for understanding the more advanced concepts covered in the course.
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  • Read foundational materials on diffusion.
  • Review lecture notes or textbooks on diffusion.
  • Solve practice problems involving Fick's Laws.
Review fundamentals of machine learning
Help you strengthen your fundamentals and improve your understanding of diffusion models.
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  • Review concepts such as supervised and unsupervised learning, loss functions, and optimization algorithms.
  • Read research papers or take online courses on the basics of machine learning.
Deep Learning
This book will provide a solid foundation in deep learning, which is essential for understanding diffusion models.
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  • Read the book
  • Take notes
  • Complete the exercises
Five other activities
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Attend meetups or conferences on diffusion models
Provide opportunities for you to connect with other professionals in this field, expanding your network and knowledge.
Browse courses on Diffusion Models
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  • Search for upcoming meetups or conferences on diffusion models.
  • Register for the event and prepare to participate actively.
Follow online tutorials on diffusion models
Help you gain a deeper understanding of diffusion models by following structured tutorials.
Browse courses on Diffusion Models
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  • Identify reputable websites or platforms that offer tutorials on diffusion models.
  • Choose a tutorial that aligns with your learning goals.
  • Follow the steps outlined in the tutorial, completing any exercises or projects.
Practice using common diffusion model packages
Give you hands-on experience working with diffusion models, enhancing your understanding of their implementation.
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  • Install and set up diffusion model packages such as OpenAI DALL-E, Google Imagen, or Stability AI.
  • Follow tutorials to learn the basics of using these packages.
  • Experiment with different diffusion model architectures and hyperparameters.
Walkthrough of diffusion model training on Vertex AI
Follow along with a guided tutorial that walks you through the process of training a diffusion model on Vertex AI. This will provide hands-on experience and a deeper understanding of the practical implementation of diffusion models.
Browse courses on Diffusion Models
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  • Find a tutorial on training diffusion models on Vertex AI.
  • Gather the necessary data and resources.
  • Follow the steps in the tutorial to train a diffusion model.
  • Evaluate the performance of the trained model.
Create a project using diffusion models
Allow you to apply your understanding of diffusion models in a practical setting, reinforcing your learning.
Browse courses on Diffusion Models
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  • Define a specific image generation problem that you want to solve.
  • Design a diffusion model architecture that is well-suited for your task.
  • Train your model using an appropriate dataset.
  • Evaluate the performance of your model against a benchmark.
  • Write a technical report describing your project.

Career center

Learners who complete Introduction to Image Generation will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
A Computer Vision Engineer designs, develops, and maintains computer vision systems. They use machine learning and other techniques to enable computers to see and interpret images and videos. Introduction to Image Generation is a great fit for a Computer Vision Engineer because it introduces diffusion models, a family of machine learning models that are popular in the field of image generation. The course covers the theory behind diffusion models and how to train and deploy them, which is valuable knowledge for a Computer Vision Engineer.
Machine Learning Engineer
A Machine Learning Engineer designs, develops, tests, deploys, and maintains machine learning models. They collaborate with data scientists to identify opportunities for applying machine learning, then build and deploy models that automate tasks and improve decision-making. Introduction to Image Generation is a great fit for a Machine Learning Engineer because it introduces diffusion models, a family of machine learning models that are popular in the field of image generation. The course covers the theory behind diffusion models and how to train and deploy them, which is valuable knowledge for a Machine Learning Engineer.
Data Analyst
A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. They use statistical and programming techniques to extract insights from data, which organizations can use to make better decisions. Introduction to Image Generation may be useful for a Data Analyst who is interested in working on image-related data. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Web Developer
A Web Developer designs and develops websites. They use programming languages and other tools to create websites that are functional, visually appealing, and easy to use. Introduction to Image Generation may be useful for a Web Developer who is working on image-related websites. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Product Manager
A Product Manager is responsible for the development and launch of new products. They work with engineers, designers, and маркетологи to define the product vision, roadmap, and marketing strategy. Introduction to Image Generation may be useful for a Product Manager who is working on image-related products. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Professor
A Professor teaches and conducts research at a college or university. They develop and deliver lectures, lead discussions, and grade assignments. Introduction to Image Generation may be useful for a Professor who is teaching in the field of image generation. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Game Developer
A Game Developer designs and develops video games. They use programming languages and other tools to create games that are fun, engaging, and visually appealing. Introduction to Image Generation may be useful for a Game Developer who is working on image-related games. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Graphic designer
A Graphic Designer creates visual concepts, using computer software or by hand, to communicate ideas that inspire, inform, and captivate consumers. They develop the overall layout and production design for various applications such as brochures, magazines, and corporate reports. Introduction to Image Generation may be useful for a Graphic Designer who is interested in using machine learning to create images. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Researcher
A Researcher conducts scientific research to advance knowledge and understanding in a particular field. They use a variety of methods to collect and analyze data, and then publish their findings in academic journals and other publications. Introduction to Image Generation may be useful for a Researcher who is working in the field of image generation. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
User Experience Designer
A User Experience Designer designs and tests user interfaces to make sure that they are easy to use and enjoyable. They work with engineers and designers to create products that meet the needs of users. Introduction to Image Generation may be useful for a User Experience Designer who is working on image-related products. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Mobile App Developer
A Mobile App Developer designs and develops mobile applications. They use programming languages and other tools to create apps that are functional, visually appealing, and easy to use. Introduction to Image Generation may be useful for a Mobile App Developer who is working on image-related apps. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Marketer
A Marketer develops and executes marketing campaigns to promote products and services. They use a variety of channels to reach target audiences, including social media, email, and paid advertising. Introduction to Image Generation may be useful for a Marketer who is working on image-related marketing campaigns. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Data Scientist
A Data Scientist uses data to uncover insights that businesses can use to make better decisions. It combines programming, mathematics and statistics to extract knowledge from data, helping organizations understand their customers, optimize operations, improve products and services, and identify new opportunities for growth. Introduction to Image Generation may be useful for a Data Scientist because it introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Scientist
A Scientist conducts scientific research to advance knowledge and understanding in a particular field. They use a variety of methods to collect and analyze data, and then publish their findings in academic journals and other publications. Introduction to Image Generation may be useful for a Scientist who is working in the field of image generation. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.
Software Engineer
A Software Engineer designs, develops, tests, and maintains software systems. They use programming languages and other tools to create software that meets the needs of users. Introduction to Image Generation may be useful for a Software Engineer who is interested in working on image generation projects. The course introduces diffusion models, a family of machine learning models that are popular in the field of image generation.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Image Generation.
Generative adversarial networks (GANs) are another type of generative model that has been widely used for image generation. Although this book focuses on GANs, it provides a valuable background for understanding the broader context of generative models and their applications.
Provides a comprehensive overview of deep learning, including its foundations, algorithms, and applications. It valuable resource for anyone interested in understanding the broader context of machine learning and its applications to image generation.
Provides a comprehensive introduction to the mathematical foundations of machine learning, including topics such as linear algebra, probability, and optimization. It valuable resource for anyone interested in gaining a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of computer vision, including topics such as image formation, feature detection, and object recognition. It valuable resource for anyone interested in gaining a deeper understanding of the practical aspects of computer vision.
Provides a comprehensive introduction to probabilistic graphical models, including topics such as Bayesian networks, Markov random fields, and factor graphs. It valuable resource for anyone interested in gaining a deeper understanding of the theoretical foundations of probabilistic graphical models.
Provides a comprehensive introduction to information theory, including topics such as entropy, mutual information, and channel capacity. It valuable resource for anyone interested in gaining a deeper understanding of the theoretical foundations of information theory.
Provides a practical guide to deep learning using Python, including topics such as neural networks, convolutional neural networks, and recurrent neural networks. It great resource for anyone interested in gaining a deeper understanding of the practical aspects of deep learning.
Provides a comprehensive introduction to statistical learning, including topics such as supervised learning, unsupervised learning, and ensemble methods. It valuable resource for anyone interested in gaining a deeper understanding of the theoretical foundations of statistical learning.
Provides a comprehensive introduction to pattern recognition and machine learning, including topics such as supervised learning, unsupervised learning, and kernel methods. It valuable resource for anyone interested in gaining a deeper understanding of the theoretical foundations of pattern recognition and machine learning.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, including topics such as Bayesian inference, Markov chain Monte Carlo, and variational inference. It valuable resource for anyone interested in gaining a deeper understanding of the theoretical foundations of machine learning.

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