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本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。
Register for this course and see more details by visiting:
OpenCourser.com/course/pqs2o7/introduction
What's inside
Syllabus
图像生成简介
本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches a novel topic in machine learning, specifically diffusion models, for image generation
Suitable for individuals with existing machine learning foundations
Provides a comprehensive understanding of diffusion models, from theory to practice
Instructors are recognized for their expertise in Google Cloud's advanced image generation models
Teaches practical skills in training and deploying diffusion models on Vertex AI
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Save Introduction to Image Generation - 简体中文 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 - 简体中文 with these
activities:
复习课程笔记和材料
Show steps
复习课程笔记和材料可以帮助巩固对扩散模型概念的理解。
Show steps
-
收集课程笔记、幻灯片和讲义
-
回顾材料并做笔记
-
总结关键概念
-
测试自己的理解
遵循扩散模型实施教程
Show steps
遵循扩散模型实施教程可以提供动手操作的经验，有助于巩固对该技术的理解。
Show steps
-
查找有关扩散模型实施的教程
-
选择一个具有清晰说明的教程
-
按照教程中的说明操作
-
在自己的数据集上测试教程中的模型
-
寻求帮助解决任何问题
与其他学生讨论扩散模型的原理
Show steps
与其他学生交流扩散模型的原理可以帮助巩固对这些概念的理解。
Show steps
-
与其他学生组成学习小组
-
选择一个扩散模型主题进行讨论
-
准备简短的介绍性陈述
-
与团队成员讨论主题
-
提出问题并寻求澄清
One other activity
Expand to see all activities and additional details
Show all four activities
自定义图像生成扩散模型的训练
Show steps
通过创建自己的图像生成扩散模型来实际应用课程中介绍的概念，从而加深对图像生成技术的理解。
Show steps
-
收集并准备图像数据集
-
选择一个扩散模型架构
-
训练扩散模型
-
评估模型的性能
-
部署模型并生成图像
复习课程笔记和材料
Show steps
复习课程笔记和材料可以帮助巩固对扩散模型概念的理解。
Show steps
Show steps
Show steps
- 收集课程笔记、幻灯片和讲义
- 回顾材料并做笔记
- 总结关键概念
- 测试自己的理解
遵循扩散模型实施教程
Show steps
遵循扩散模型实施教程可以提供动手操作的经验，有助于巩固对该技术的理解。
Show steps
Show steps
Show steps
- 查找有关扩散模型实施的教程
- 选择一个具有清晰说明的教程
- 按照教程中的说明操作
- 在自己的数据集上测试教程中的模型
- 寻求帮助解决任何问题
与其他学生讨论扩散模型的原理
Show steps
与其他学生交流扩散模型的原理可以帮助巩固对这些概念的理解。
Show steps
Show steps
Show steps
- 与其他学生组成学习小组
- 选择一个扩散模型主题进行讨论
- 准备简短的介绍性陈述
- 与团队成员讨论主题
- 提出问题并寻求澄清
One other activity
Expand to see all activities and additional details
Show all four activities
自定义图像生成扩散模型的训练
Show steps
通过创建自己的图像生成扩散模型来实际应用课程中介绍的概念，从而加深对图像生成技术的理解。
Show steps
Show steps
Show steps
- 收集并准备图像数据集
- 选择一个扩散模型架构
- 训练扩散模型
- 评估模型的性能
- 部署模型并生成图像
Career center
Learners who complete Introduction to Image Generation - 简体中文 will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer is a data professional who is primarily responsible for building the data infrastructure and data pipelines that are used by Data Scientists and other business stakeholders to access, manage, and use data. As part of this role, you might use machine learning models and techniques to wrangle data for an organization's specific needs, and this course may be useful.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models into production, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Data Analyst
Data Analyst
Data Analysts are responsible for cleaning, transforming, and analyzing data to extract valuable insights for businesses, and this course may be useful. Many Data Analysts work with machine learning models to improve the efficiency and accuracy of their work, and this course will help build a foundation in the field of generative models.
Software Engineer (Machine Learning)
Software Engineer (Machine Learning)
Software Engineers who specialize in Machine Learning are responsible for developing and deploying machine learning models, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Research Scientist: Machine Learning
Research Scientist: Machine Learning
Research Scientists who specialize in Machine Learning are responsible for developing new machine learning algorithms and techniques, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers are responsible for developing and deploying computer vision systems, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Data Scientist
Data Scientist
Data Scientists are responsible for developing machine learning models and algorithms, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Applied Scientist - Machine Learning
Applied Scientist - Machine Learning
Applied Scientists who specialize in Machine Learning are responsible for developing and deploying machine learning models, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers are responsible for developing and deploying AI systems, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Machine Learning Architect
Machine Learning Architect
Machine Learning Architects are responsible for designing and implementing machine learning systems, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Computer Vision Researcher
Computer Vision Researcher
Computer Vision Researchers are responsible for developing new computer vision algorithms and techniques, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers are responsible for developing and deploying NLP systems, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Quantitative Analyst (Machine Learning)
Quantitative Analyst (Machine Learning)
Quantitative Analysts who specialize in Machine Learning are responsible for developing and deploying machine learning models, and this course may be useful for this career role. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
Blockchain Developer
Blockchain Developer
Blockchain Developers are responsible for designing and developing blockchain systems, and this course may be useful for this career path. The blockchain is a distributed ledger system that is used to record transactions across many computers so that any involved record cannot be altered retroactively, without the alteration of all subsequent blocks. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
User Experience Designer
User Experience Designer
User Experience Designers are responsible for designing and developing user interfaces, and this course may be useful for this career path. This course will help build a foundation in the theoretical underpinnings of diffusion models, which are a recently developed type of machine learning model that has shown great promise for image generation tasks.
For more career information including salaries, visit:
OpenCourser.com/course/pqs2o7/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 - 简体中文.
Save
Provides a comprehensive overview of generative adversarial networks (GANs), a type of deep learning model that can generate new data from a given distribution. It covers the theoretical foundations of GANs, different GAN architectures, and training and evaluation techniques. It valuable resource for anyone looking to learn more about GANs and their applications in image generation.
这本书提供了计算机视觉中深度学习的全面概述，是图像生成等高级计算机视觉技术的背景知识。
Save
Provides a comprehensive overview of deep learning, a type of machine learning that uses artificial neural networks to learn from data. It covers the theoretical foundations of deep learning, different deep learning architectures, and training and evaluation techniques. It valuable resource for anyone looking to learn more about deep learning and its applications in image generation.
这本书提供了数字图像处理的全面概述，是图像生成领域的重要背景知识。
Save
本书提供了计算机图形学领域的全面概述，是图像生成领域的重要背景知识。
这本书提供了模式识别和机器学习的全面概述，是扩散模型等高级机器学习模型的基础。
这本书提供了信息论、推理和学习算法的全面概述，是扩散模型等高级机器学习模型的基础。
Save
这本书介绍了图像生成的理论和实践。它涵盖了图像生成的不同方法，包括传统方法和深度学习方法。它是一本宝贵的资源，适合任何希望学习图像生成及其在图像生成中的应用的人。
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers the basic concepts of computer vision, such as image formation, feature extraction, and object recognition. It valuable resource for anyone looking to learn more about computer vision and its applications in image generation.
Save
本书全面介绍了深度学习原理及应用，涵盖了神经网络基础、卷积神经网络、循环神经网络、生成对抗网络等内容，是一本深度学习入门及进阶的优秀参考书。
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers the basic concepts of machine learning, such as probability theory, Bayesian inference, and graphical models. It valuable resource for anyone looking to learn more about machine learning and its applications in image generation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pqs2o7/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
中文
Transcript
English
Good to know
Teaches a novel topic in machine learning, specifically diffusion models, for image generation
Suitable for individuals with existing machine learning foundations
Provides a comprehensive understanding of diffusion models, from theory to practice
Instructors are recognized for their expertise in Google Cloud's advanced image generation models
Teaches practical skills in training and deploying diffusion models on Vertex AI
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