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Google Cloud Training
Neste curso, apresentamos os modelos de difusão, uma família de modelos de machine learning promissora no campo da geração de imagens. Os modelos de difusão são baseados na física, mais especificamente na termodinâmica. Nos últimos anos, eles se popularizaram no setor e nas pesquisas. Esses modelos servem de base para ferramentas e modelos avançados de geração de imagem no Google Cloud. Este curso é uma introdução à teoria dos modelos de difusão e como eles devem ser treinados e implantados na Vertex AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/qggzei/introduction
What's inside
Syllabus
Introdução à geração de imagens
Neste curso, apresentamos os modelos de difusão, uma família de modelos de machine learning promissora no campo da geração de imagens. Os modelos de difusão são baseados na física, mais especificamente na termodinâmica. Nos últimos anos, eles se popularizaram no setor e nas pesquisas. Esses modelos servem de base para ferramentas e modelos avançados de geração de imagem no Google Cloud. Este curso é uma introdução à teoria dos modelos de difusão e como eles devem ser treinados e implantados na Vertex AI.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Desenvolvido pela Google Cloud Training, que é uma empresa do Google, um líder mundial no desenvolvimento de inteligência artificial e machine learning, o que demonstra a qualidade do curso
Aborda os modelos de difusão, uma abordagem inovadora na geração de imagens que ganhou popularidade no mercado e na pesquisa, o que indica a atualidade do curso
Fornece uma base para ferramentas e modelos avançados de geração de imagem no Google Cloud, o que sugere que o curso é relevante para profissionais que procuram usar essas tecnologias
Ensina os fundamentos dos modelos de difusão, o que é ideal para iniciantes que buscam uma compreensão básica dessa tecnologia
Save this course
Save Introduction to Image Generation - Português Brasileiro to your list so you can find it easily later:
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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 - Português Brasileiro with these
activities:
Revise Basic Image Generation Concepts
Show steps
Reinforce your understanding of image generation fundamentals to help you grasp advanced concepts in diffusion models.
Browse courses on
Image Generation
Show steps
-
Review image file formats, color models, and transformations.
-
Explore basic image generation algorithms, such as Generative Adversarial Networks (GANs).
Attend Diffusion Model Workshops
Show steps
Gain insights from experts and connect with professionals in the field to enhance your understanding of diffusion models.
Show steps
-
Identify relevant workshops on diffusion models.
-
Register for the workshops.
-
Attend the workshops and actively participate in discussions.
Work through Diffusion Model Implementation Tutorials
Show steps
Practice implementing diffusion models to strengthen your understanding of their architecture and training process.
Show steps
-
Find tutorials on building diffusion models using frameworks like PyTorch or TensorFlow.
-
Follow the tutorials step-by-step, experimenting with different hyperparameters.
-
Troubleshoot any errors and optimize your model's performance.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Diffusion Model Exercises
Show steps
Test your understanding of diffusion models by solving practice problems involving their training and evaluation.
Show steps
-
Find online resources or textbooks with diffusion model exercises.
-
Attempt to solve the exercises, focusing on applying the concepts you've learned.
-
Review your solutions and identify areas where you need further clarification.
Experiment with Diffusion Model Variations
Show steps
Expand your knowledge by exploring variations of diffusion models, such as conditional diffusion or score-based diffusion.
Show steps
-
Research different diffusion model variations and their applications.
-
Implement one or more variations in a project.
-
Compare the performance of the variations and analyze the results.
Build a Diffusion Model Application
Show steps
Deepen your understanding by building a practical diffusion model application, such as an image generator or an image denoiser.
Show steps
-
Define the problem you want to solve with a diffusion model.
-
Design and implement the diffusion model architecture.
-
Train and evaluate the model on a suitable dataset.
-
Deploy the model and test its performance in a real-world setting.
Participate in Diffusion Model Hackathons or Challenges
Show steps
Challenge yourself in a competitive environment to push your limits and gain exposure to cutting-edge diffusion model applications.
Show steps
-
Identify relevant hackathons or challenges focused on diffusion models.
-
Form a team or participate individually.
-
Develop an innovative diffusion model-based solution.
-
Submit your solution and present your findings.
Contribute to Diffusion Model Open-Source Projects
Show steps
Make valuable contributions to the diffusion model community by participating in open-source projects.
Show steps
-
Identify active open-source projects related to diffusion models.
-
Review the project documentation and identify areas where you can contribute.
-
Submit pull requests with your contributions, ensuring they are well-tested and documented.
Revise Basic Image Generation Concepts
Show steps
Reinforce your understanding of image generation fundamentals to help you grasp advanced concepts in diffusion models.
Browse courses on
Image Generation
Show steps
Show steps
Show steps
- Review image file formats, color models, and transformations.
- Explore basic image generation algorithms, such as Generative Adversarial Networks (GANs).
Attend Diffusion Model Workshops
Show steps
Gain insights from experts and connect with professionals in the field to enhance your understanding of diffusion models.
Show steps
Show steps
Show steps
- Identify relevant workshops on diffusion models.
- Register for the workshops.
- Attend the workshops and actively participate in discussions.
Work through Diffusion Model Implementation Tutorials
Show steps
Practice implementing diffusion models to strengthen your understanding of their architecture and training process.
Show steps
Show steps
Show steps
- Find tutorials on building diffusion models using frameworks like PyTorch or TensorFlow.
- Follow the tutorials step-by-step, experimenting with different hyperparameters.
- Troubleshoot any errors and optimize your model's performance.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Diffusion Model Exercises
Show steps
Test your understanding of diffusion models by solving practice problems involving their training and evaluation.
Show steps
Show steps
Show steps
- Find online resources or textbooks with diffusion model exercises.
- Attempt to solve the exercises, focusing on applying the concepts you've learned.
- Review your solutions and identify areas where you need further clarification.
Experiment with Diffusion Model Variations
Show steps
Expand your knowledge by exploring variations of diffusion models, such as conditional diffusion or score-based diffusion.
Show steps
Show steps
Show steps
- Research different diffusion model variations and their applications.
- Implement one or more variations in a project.
- Compare the performance of the variations and analyze the results.
Build a Diffusion Model Application
Show steps
Deepen your understanding by building a practical diffusion model application, such as an image generator or an image denoiser.
Show steps
Show steps
Show steps
- Define the problem you want to solve with a diffusion model.
- Design and implement the diffusion model architecture.
- Train and evaluate the model on a suitable dataset.
- Deploy the model and test its performance in a real-world setting.
Participate in Diffusion Model Hackathons or Challenges
Show steps
Challenge yourself in a competitive environment to push your limits and gain exposure to cutting-edge diffusion model applications.
Show steps
Show steps
Show steps
- Identify relevant hackathons or challenges focused on diffusion models.
- Form a team or participate individually.
- Develop an innovative diffusion model-based solution.
- Submit your solution and present your findings.
Contribute to Diffusion Model Open-Source Projects
Show steps
Make valuable contributions to the diffusion model community by participating in open-source projects.
Show steps
Show steps
Show steps
- Identify active open-source projects related to diffusion models.
- Review the project documentation and identify areas where you can contribute.
- Submit pull requests with your contributions, ensuring they are well-tested and documented.
Career center
Learners who complete Introduction to Image Generation - Português Brasileiro will develop knowledge and skills
that may be useful to these careers:
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of scientific fields, including machine learning and computer vision. This course may be useful for Research Scientists who are interested in exploring the use of diffusion models for image generation. The course provides a comprehensive overview of the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Research Scientists who want to conduct research on image generation and develop new applications for this technology.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course may be useful for Machine Learning Engineers who are interested in working with diffusion models for image generation. The course provides a thorough introduction to the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Machine Learning Engineers who want to build and deploy image generation models for a variety of applications.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems that can interpret and understand visual data. This course may be useful for Computer Vision Engineers who are interested in using diffusion models for image generation. The course provides a solid foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Computer Vision Engineers who want to build and deploy image generation systems for a variety of applications.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course may be useful for Data Analysts who are interested in using diffusion models for image generation. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Data Analysts who want to use image generation to enhance their data analysis and visualization capabilities.
Web Developer
Web Developer
Web Developers design and develop websites. This course may be useful for Web Developers who are interested in using image generation to create dynamic and interactive websites. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Web Developers who want to use image generation to create websites that are both visually appealing and engaging.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers who are interested in developing image generation products. The course provides a solid understanding of the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Product Managers who want to build and launch image generation products that meet the needs of their customers.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. This course may be useful for Technical Writers who are interested in using image generation to create visual aids for their documentation. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Technical Writers who want to use image generation to create high-quality visual aids that will help their readers understand complex technical concepts.
UX Designer
UX Designer
UX Designers are responsible for designing the user experience of products and services. This course may be useful for UX Designers who are interested in using image generation to create user interfaces. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for UX Designers who want to use image generation to create user interfaces that are both visually appealing and easy to use.
Graphic designer
Graphic designer
Graphic Designers create visual concepts and designs for a variety of media. This course may be useful for Graphic Designers who are interested in using image generation to create new and innovative designs. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Graphic Designers who want to use image generation to create designs that are both visually appealing and effective.
Photographer
Photographer
Photographers take pictures of people, places, and things. This course may be useful for Photographers who are interested in using image generation to enhance their work. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Photographers who want to use image generation to create new and innovative images that will stand out from the crowd.
Educator
Educator
Educators teach students in a variety of settings. This course may be useful for Educators who are interested in using image generation to create educational materials. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Educators who want to use image generation to create engaging and interactive educational materials that will help their students learn.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course may be useful for Marketing Managers who are interested in using image generation to create marketing materials. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Marketing Managers who want to use image generation to create high-quality marketing materials that will engage their target audience.
Artist
Artist
Artists create visual works of art. This course may be useful for Artists who are interested in using image generation to create new works of art. The course provides a foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Artists who want to use image generation to explore new creative possibilities and push the boundaries of their art.
Data Scientist
Data Scientist
Data Scientists use machine learning to solve advanced analytical problems and make informed data-driven decisions. This course may be useful for aspiring Data Scientists as it will provide a foundation in the theory of diffusion models, which are a family of machine learning models that are particularly well-suited for image generation. Understanding these models can be advantageous in the field of data science, as they can be used to generate synthetic data, enhance existing images, or create new images from scratch. Additionally, this course covers how to train and deploy diffusion models using Vertex AI, which is a cloud-based platform for machine learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers who are interested in working with diffusion models for image generation. The course provides a solid foundation in the theory of diffusion models, as well as how to train and deploy them using Vertex AI. This knowledge can be valuable for Software Engineers who want to build and deploy image generation systems for a variety of applications.
For more career information including salaries, visit:
OpenCourser.com/course/qggzei/introduction
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 - Português Brasileiro.
Save
Provides a comprehensive overview of GANs, including their history, theory, and applications. It valuable resource for anyone interested in learning more about this topic.
Save
Provides a comprehensive overview of deep learning, including its history, theory, and applications. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of statistical learning, including its history, theory, and applications. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of pattern recognition and machine learning, including its history, theory, and applications. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of machine learning, including its history, theory, and applications. It valuable resource for anyone interested in learning more about this topic.
Provides a practical guide to machine learning, including how to choose the right algorithms, train models, and evaluate results. It valuable resource for anyone interested in learning more about this topic.
Provides a practical guide to artificial intelligence, including how to choose the right algorithms, train models, and evaluate results. It valuable resource for anyone interested in learning more about this topic.
Provides a practical guide to deep learning, including how to choose the right algorithms, train models, and evaluate results. It valuable resource for anyone interested in learning more about this topic.
Provides a practical guide to machine learning, including how to choose the right algorithms, train models, and evaluate results. It valuable resource for anyone interested in learning more about this topic.
Provides a practical guide to machine learning, including how to choose the right algorithms, train models, and evaluate results. It valuable resource for anyone interested in learning more about this topic.
Provides a practical guide to machine learning, including how to choose the right algorithms, train models, and evaluate results. It valuable resource for anyone interested in learning more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qggzei/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
Português
Transcript
English
Good to know
Desenvolvido pela Google Cloud Training, que é uma empresa do Google, um líder mundial no desenvolvimento de inteligência artificial e machine learning, o que demonstra a qualidade do curso
Aborda os modelos de difusão, uma abordagem inovadora na geração de imagens que ganhou popularidade no mercado e na pesquisa, o que indica a atualidade do curso
Fornece uma base para ferramentas e modelos avançados de geração de imagem no Google Cloud, o que sugere que o curso é relevante para profissionais que procuram usar essas tecnologias
Ensina os fundamentos dos modelos de difusão, o que é ideal para iniciantes que buscam uma compreensão básica dessa tecnologia
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