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このコースでは拡散モデルについて説明します。拡散モデルは ML モデル ファミリーの一つで、最近、画像生成分野での有望性が示されました。拡散モデルは物理学、特に熱力学からインスピレーションを得ています。ここ数年、拡散モデルは研究と産業界の両方で広まりました。拡散モデルは、Google Cloud の最先端の画像生成モデルやツールの多くを支える技術です。このコースでは、拡散モデルの背景にある理論と、モデルを Vertex AI でトレーニングしてデプロイする方法について説明します。
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores the widely-used diffusion model in generative AI
Provides a comprehensive foundation in diffusion model concepts
Imparts expertise in training and deploying diffusion models on Vertex AI
Facilitates hands-on learning through interactive materials and labs
Led by Google Cloud Training, renowned experts in the field
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Reviews summary
画像生成とvertex aiの基礎
学習者によると、このコースは拡散モデルの理論を明確に理解するのに非常に役立ち、Vertex AIでの実践的な画像生成モデルのトレーニングとデプロイ方法を学ぶことができると高く評価されています。内容はよく構成されており、画像生成分野への優れた入門として基礎を固めるのに最適です。ただし、一部の学習者は機械学習やPythonの前提知識があった方がスムーズに進められると感じており、より高度な応用例や深掘りを期待すると物足りないかもしれません。また、演習環境のセットアップに多少の注意とトラブルシューティングが必要な場合があるとの声も聞かれます。
Vertex AIを使ったモデルトレーニングとデプロイを実践できます。
"Vertex AIでのハンズオン演習は、理論を実践に落とし込むのに非常に役立ちました。すぐに業務で使えそうです。"
"Google Cloudの最新の画像生成ツールが、具体的なコードと手順で示されており、実用性が高いと感じました。"
"デプロイのプロセスまで一貫して学べるのは、他のコースにはない大きな利点だと思います。"
拡散モデルの複雑な理論を分かりやすく解説しています。
"拡散モデルの概念が非常に分かりやすく説明されていて、理論的な背景をしっかり理解できました。"
"難解に思える物理学からのインスピレーションや数学的な側面も、丁寧な解説でスムーズに頭に入りました。"
"これまで漠然としていた拡散モデルの仕組みが、このコースで明確に紐解かれ、基本が固まりました。"
入門レベルのため、高度な応用やカスタマイズには不足します。
"基礎を学ぶには素晴らしいですが、具体的な応用例やモデルのチューニングに関する詳細がもっと欲しかったです。"
"コースの範囲は入門に限定されており、研究レベルの深い内容やトラブルシューティングの高度なテクニックは期待できませんでした。"
"私はさらなる発展的な内容を期待していましたが、そこは別のコースや資料で補完する必要がありそうです。"
機械学習やPythonの基本知識が推奨されます。
"コース自体は入門ですが、機械学習の基本的な用語やPythonの経験があると、よりスムーズに理解できると私は感じました。"
"私は全くの初心者でしたが、最初の数モジュールで少し戸惑いました。予習しておけばよかったと思います。"
"理論説明は丁寧ですが、コードの理解にはある程度のプログラミングスキルが私には必要だと感じました。"
演習環境の準備に一部課題が見られる可能性があります。
"ハンズオンのラボ環境を構築する際に、いくつかのエラーに遭遇しました。もう少し詳細な手順書があれば助かったです。"
"Vertex AIの環境設定に少し手間取りましたが、何とか解決できました。Cloud経験者なら問題ないと思います。"
"手順通りに進めても動かない部分があり、自己解決が必要な場面がありました。この点は改善されると嬉しいです。"
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 - 日本語版 with these
activities:
Find a mentor who can provide guidance on diffusion models
Show steps
Finding a mentor can provide you with support and guidance as you learn about diffusion models.
Browse courses on
Diffusion Models
Show steps
-
Identify potential mentors
-
Reach out to mentors
-
Meet with mentors
Review basic probability and statistics
Show steps
Reviewing basic probability and statistics will help you understand the fundamentals of ML models and how they can be used to generate images.
Browse courses on
Probability
Show steps
-
Review lecture notes
-
Complete practice problems
Tutorial on the basics of diffusion models
Show steps
This tutorial will teach you the basics of diffusion models and will help you understand how they work.
Browse courses on
Diffusion Models
Show steps
-
Watch the video tutorial
-
Follow along with the coding examples
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice exercises on diffusion models
Show steps
These exercises will help you practice the concepts you learned in the tutorial and will help you understand how to use diffusion models to generate images.
Browse courses on
Diffusion Models
Show steps
-
Complete the coding exercises
-
Debug your code
Volunteer at a local AI or data science meetup
Show steps
Volunteering at a meetup will allow you to meet other people who are interested in AI and data science, and will help you to stay up-to-date on the latest trends.
Browse courses on
AI
Show steps
-
Find a local meetup
-
Contact the organizers
-
Attend the meetup
Project: Generate images using a diffusion model
Show steps
This project will allow you to apply the concepts you learned in the tutorial and practice exercises to a real-world problem.
Browse courses on
Diffusion Models
Show steps
-
Define the problem
-
Gather data
-
Train the model
-
Evaluate the model
Blog post: How to use diffusion models to generate images
Show steps
Writing a blog post will help you to synthesize what you have learned and share your knowledge with others.
Browse courses on
Diffusion Models
Show steps
-
Choose a topic
-
Research your topic
-
Write your blog post
-
Publish your blog post
Kaggle competition: Image generation using diffusion models
Show steps
Participating in a competition will allow you to test your skills against others and will help you to learn from others.
Browse courses on
Diffusion Models
Show steps
-
Register for the competition
-
Download the data
-
Develop your model
-
Submit your results
Find a mentor who can provide guidance on diffusion models
Show steps
Finding a mentor can provide you with support and guidance as you learn about diffusion models.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Identify potential mentors
- Reach out to mentors
- Meet with mentors
Review basic probability and statistics
Show steps
Reviewing basic probability and statistics will help you understand the fundamentals of ML models and how they can be used to generate images.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review lecture notes
- Complete practice problems
Tutorial on the basics of diffusion models
Show steps
This tutorial will teach you the basics of diffusion models and will help you understand how they work.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Watch the video tutorial
- Follow along with the coding examples
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice exercises on diffusion models
Show steps
These exercises will help you practice the concepts you learned in the tutorial and will help you understand how to use diffusion models to generate images.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Complete the coding exercises
- Debug your code
Volunteer at a local AI or data science meetup
Show steps
Volunteering at a meetup will allow you to meet other people who are interested in AI and data science, and will help you to stay up-to-date on the latest trends.
Browse courses on
AI
Show steps
Show steps
Show steps
- Find a local meetup
- Contact the organizers
- Attend the meetup
Project: Generate images using a diffusion model
Show steps
This project will allow you to apply the concepts you learned in the tutorial and practice exercises to a real-world problem.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Define the problem
- Gather data
- Train the model
- Evaluate the model
Blog post: How to use diffusion models to generate images
Show steps
Writing a blog post will help you to synthesize what you have learned and share your knowledge with others.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Choose a topic
- Research your topic
- Write your blog post
- Publish your blog post
Kaggle competition: Image generation using diffusion models
Show steps
Participating in a competition will allow you to test your skills against others and will help you to learn from others.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Register for the competition
- Download the data
- Develop your model
- Submit your results
Career center
Learners who complete Introduction to Image Generation - 日本語版 will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, build, and maintain computer vision systems, which enable computers to see and interpret the world around them. This course may be useful to aspiring Computer Vision Engineers as they work to develop image-based applications, particularly those involving diffusion models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models to solve complex problems in various industries. An advanced degree is not typically required, but this course may be helpful for aspiring Machine Learning Engineers who wish to specialize in image processing and generation.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, build, and maintain artificial intelligence models and systems. This course covers diffusion models, which are a growing subfield of AI, providing a helpful foundation for aspiring engineers.
Software Developer
Software Developer
Software Developers design, build, test, and maintain the software applications and systems that we use every day. While an advanced degree is usually not required, those working with image-based applications may find this course helpful as they explore the use of diffusion models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course may be useful to aspiring Data Analysts who are interested in exploring new applications for diffusion models.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and features. This course may be helpful for aspiring Product Managers who are interested in image processing and generation, as it will provide them with a deeper understanding of the underlying technology.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. Data Scientists typically need at least a bachelor's degree in computer science, statistics, or a related field. This course may help aspiring Data Scientists better understand the foundation of a group of models increasingly used in their line of work.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. This course may be useful to aspiring Quantitative Analysts who are interested in using diffusion models to develop new trading strategies.
Supply Chain Analyst
Supply Chain Analyst
Supply Chain Analysts analyze supply chains to improve efficiency and reduce costs. This course may help aspiring Supply Chain Analysts develop image processing and generation solutions that optimize supply chains.
User Experience Researcher
User Experience Researcher
The work of a User Experience Researcher is to understand how users interact with products and services, and to use their findings to improve the user experience. This course may be of interest to User Experience Researchers who are interested in image processing and generation.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make informed decisions. This course may help aspiring Business Analysts develop image processing and generation solutions for businesses seeking to improve their operations.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to evaluate and make recommendations on investments. This course may be useful to aspiring Financial Analysts who are interested in using diffusion models to develop new investment strategies or improve risk assessment.
Market Researcher
Market Researcher
Market Researchers study market trends and consumer behavior. This course may be useful to aspiring Market Researchers who are interested in using diffusion models to analyze market data and identify new opportunities.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course may help aspiring Operations Research Analysts develop new solutions using diffusion models.
Research Scientist
Research Scientist
A Research Scientist focuses on applying scientific research to the practical problems faced by industry or government. Scientists who specialize in fields like computer science and statistics typically need an advanced degree, particularly those who wish to lead research projects or design new products. This specialization in Image Generation may be useful in this line of work.
For more career information including salaries, visit:
OpenCourser.com/course/kl01ih/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 - 日本語版.
Save
Provides a comprehensive overview of generative adversarial networks (GANs), including their theoretical foundations, training algorithms, and applications. It valuable resource for researchers and practitioners interested in learning more about GANs.
Save
この本は、画像処理に使用される畳み込みニューラルネットワークについて説明しています。畳み込みニューラルネットワークは、画像生成の基礎技術です。
Provides a comprehensive overview of deep learning for computer vision, including image classification, object detection, and segmentation. It valuable resource for researchers and practitioners interested in learning more about deep learning for computer vision.
Save
Provides a comprehensive overview of computer vision, including image formation, feature detection, and object recognition. It valuable resource for researchers and practitioners interested in learning more about computer vision.
この本は、パターン認識と機械学習について説明しています。パターン認識と機械学習は、画像生成に関連する分野です。
Provides a comprehensive overview of digital image processing, including image acquisition, image enhancement, and image analysis. It valuable resource for researchers and practitioners interested in learning more about digital image processing.
この本は、人工知能について説明しています。人工知能は、画像生成に使用される基礎技術です。
Provides a comprehensive overview of mathematical methods for image processing, including image transforms, image filtering, and image segmentation. It valuable resource for researchers and practitioners interested in learning more about mathematical methods for image processing.
この本は、強化学習について説明しています。強化学習は、画像生成に使用される一般的な手法です。
この本は、ベイジアン法について説明しています。ベイジアン法は、画像生成に使用される一般的な手法です。
この本は、確率的グラフィカルモデルについて説明しています。確率的グラフィカルモデルは、画像生成に使用される一般的な手法です。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kl01ih/introduction
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