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Google Cloud Training
Take Udacity's free Cloud Introduction to Image Generation Course by Google and learn what diffusion models are, how they work, and real use-cases for diffusion models.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Intermediate Python
- Basic machine learning
- Convolutional neural networks
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/qhsgwt/introduction
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What's inside
Syllabus
This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Appeals to AI practitioners familiar with machine learning and deep learning concepts, with training in diffusion models
Taught by Google Cloud Training (Google)
Develops practical skills in how to apply diffusion models to generate images
Assumes prior knowledge in Python programming, basic ML, and CNNs
Save this course
Save Introduction to Image Generation with Google Cloud 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 with Google Cloud with these
activities:
Review convolutional neural networks
Show steps
Reinforce understanding of convolutional neural networks, which are essential for understanding diffusion models.
Browse courses on
Convolutional Neural Networks
Show steps
-
Review lecture notes or textbooks on CNN architecture and operations.
-
Complete online quizzes or practice exercises on CNNs.
-
Apply CNNs to a simple image classification task using a library like TensorFlow or PyTorch.
Review Python fundamentals
Show steps
Review basic Python syntax and concepts to ensure a strong foundation for the course.
Browse courses on
Python
Show steps
-
Reread Python documentation on data types, operators, and control flow.
-
Complete online Python tutorials on basic data structures and algorithms.
-
Solve coding challenges on platforms like LeetCode or HackerRank.
Compile a collection of resources on diffusion models
Show steps
Gather and organize a comprehensive set of resources, including research papers, tutorials, and code repositories, to support ongoing learning.
Browse courses on
Research
Show steps
-
Conduct a thorough online search for relevant resources.
-
Organize the resources into a structured format, such as a website or shared document.
-
Share the collection with other learners and the community.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group or discussion forum
Show steps
Engage with peers to discuss course concepts, share insights, and work through problems together.
Show steps
-
Join an existing study group or discussion forum related to diffusion models.
-
Start a discussion thread on a specific topic or challenge.
-
Participate in online or in-person meetups to connect with other learners.
Explore diffusion models using PyTorch tutorials
Show steps
Follow online tutorials and workshops to gain hands-on experience with implementing diffusion models using PyTorch.
Browse courses on
PyTorch
Show steps
-
Follow a PyTorch tutorial on building a simple diffusion model from scratch.
-
Attend a workshop or webinar on advanced diffusion model techniques.
-
Experiment with different model architectures and training parameters.
Contribute to open-source diffusion model projects
Show steps
Gain practical experience and contribute to the diffusion model community by participating in open-source projects.
Browse courses on
Open-Source
Show steps
-
Identify open-source diffusion model projects that align with your interests.
-
Join the project's community and contribute to discussions.
-
Submit bug reports, feature requests, or code contributions.
Build a diffusion model for a specific application
Show steps
Apply diffusion models to a practical problem by building a model for a specific image generation task, such as generating realistic faces or enhancing low-resolution images.
Browse courses on
Image Generation
Show steps
-
Identify a specific image generation application and define the problem statement.
-
Design and implement a diffusion model architecture tailored to the application.
-
Train and evaluate the model on a relevant dataset.
-
Present the results and insights gained from the project.
Participate in a diffusion model competition
Show steps
Challenge yourself and test your skills by participating in a diffusion model competition to push your limits.
Browse courses on
Diffusion Models
Show steps
-
Research different diffusion model competitions and select one to participate in.
-
Develop a strategy and approach for building and training a competitive model.
-
Submit your model and presentation for evaluation.
-
Analyze the results and reflect on areas for improvement.
Review convolutional neural networks
Show steps
Reinforce understanding of convolutional neural networks, which are essential for understanding diffusion models.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on CNN architecture and operations.
- Complete online quizzes or practice exercises on CNNs.
- Apply CNNs to a simple image classification task using a library like TensorFlow or PyTorch.
Review Python fundamentals
Show steps
Review basic Python syntax and concepts to ensure a strong foundation for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Reread Python documentation on data types, operators, and control flow.
- Complete online Python tutorials on basic data structures and algorithms.
- Solve coding challenges on platforms like LeetCode or HackerRank.
Compile a collection of resources on diffusion models
Show steps
Gather and organize a comprehensive set of resources, including research papers, tutorials, and code repositories, to support ongoing learning.
Browse courses on
Research
Show steps
Show steps
Show steps
- Conduct a thorough online search for relevant resources.
- Organize the resources into a structured format, such as a website or shared document.
- Share the collection with other learners and the community.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group or discussion forum
Show steps
Engage with peers to discuss course concepts, share insights, and work through problems together.
Show steps
Show steps
Show steps
- Join an existing study group or discussion forum related to diffusion models.
- Start a discussion thread on a specific topic or challenge.
- Participate in online or in-person meetups to connect with other learners.
Explore diffusion models using PyTorch tutorials
Show steps
Follow online tutorials and workshops to gain hands-on experience with implementing diffusion models using PyTorch.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Follow a PyTorch tutorial on building a simple diffusion model from scratch.
- Attend a workshop or webinar on advanced diffusion model techniques.
- Experiment with different model architectures and training parameters.
Contribute to open-source diffusion model projects
Show steps
Gain practical experience and contribute to the diffusion model community by participating in open-source projects.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Identify open-source diffusion model projects that align with your interests.
- Join the project's community and contribute to discussions.
- Submit bug reports, feature requests, or code contributions.
Build a diffusion model for a specific application
Show steps
Apply diffusion models to a practical problem by building a model for a specific image generation task, such as generating realistic faces or enhancing low-resolution images.
Browse courses on
Image Generation
Show steps
Show steps
Show steps
- Identify a specific image generation application and define the problem statement.
- Design and implement a diffusion model architecture tailored to the application.
- Train and evaluate the model on a relevant dataset.
- Present the results and insights gained from the project.
Participate in a diffusion model competition
Show steps
Challenge yourself and test your skills by participating in a diffusion model competition to push your limits.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Research different diffusion model competitions and select one to participate in.
- Develop a strategy and approach for building and training a competitive model.
- Submit your model and presentation for evaluation.
- Analyze the results and reflect on areas for improvement.
Career center
Learners who complete Introduction to Image Generation with Google Cloud will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning systems. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Familiarity with these models may be useful for success as a Machine Learning Engineer.
Research Scientist
Research Scientist
Research Scientists lead the research and development of new technologies and products. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Research Scientists may find this information useful in their work.
Data Scientist
Data Scientist
Data Scientists work to combine programming skills, math, and statistics to investigate data, build models, and solve problems for businesses. This course may be useful for Data Scientists because it introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space.
Software Engineer
Software Engineer
Software Engineers apply engineering principles to the design, development, implementation, testing, and maintenance of software systems. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Software Engineers may find this course's information useful for building software involving image generation.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make informed decisions. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Data Analysts may find this course's information useful for working with image data.
Product Manager
Product Manager
Product Managers are responsible for the development and execution of product strategy. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Product Managers may find this course's information useful for understanding the potential applications of this technology.
Business Analyst
Business Analyst
Business Analysts use data and technology to solve business problems. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Business Analysts may find this course's information useful for understanding the potential applications of this technology.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Information Security Analysts may find this course's information useful for understanding the potential applications of this technology for image security.
Computer Systems Analyst
Computer Systems Analyst
Computer Systems Analysts study the needs of an organization in order to implement computer systems. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Computer Systems Analysts may find this course's information useful for understanding the potential applications of this technology.
Network Administrator
Network Administrator
Network Administrators maintain and troubleshoot computer networks. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Network Administrators may find this course's information useful for understanding the potential applications of this technology.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Database Administrators may find this course's information useful for understanding the potential applications of this technology.
Technical Writer
Technical Writer
Technical Writers create instruction manuals, technical reports, and other documentation for a variety of audiences. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Technical Writers may find this course's information useful for understanding the potential applications of this technology.
Web Developer
Web Developer
Web Developers create and maintain websites. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Web Developers may find this course's information useful for understanding the potential applications of this technology.
UX Designer
UX Designer
UX Designers create user interfaces for websites and other digital products. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. UX Designers may find this course's information useful for understanding the potential applications of this technology.
Graphic designer
Graphic designer
Graphic Designers create visual concepts for a variety of purposes, including advertising, marketing, and web design. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Graphic Designers may find this course's information useful for understanding the potential applications of this technology.
For more career information including salaries, visit:
OpenCourser.com/course/qhsgwt/introduction
Reading list
We've selected eight 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 with Google Cloud.
Provides a broad overview of deep learning. Also provides many interesting examples of deep learning in use.
Save
Provides a deep dive into generative adversarial networks, a technology related to diffusion models.
Save
Provides a broad overview of deep learning, a technology related to diffusion models.
Provides a mathematical background for information theory and related topics in machine learning and AI.
Save
Provides a broad overview of computer vision, including topics like image generation.
Provides an introduction to convex optimization, a technique used in many machine learning applications.
Introduces reinforcement learning, a technique used in some machine learning and AI applications.
Provides a foundation in the math used in many machine learning techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qhsgwt/introduction
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Effort
1 hour
Via
Udacity
Instructor
Google Cloud Training
Language
English
Good to know
Appeals to AI practitioners familiar with machine learning and deep learning concepts, with training in diffusion models
Taught by Google Cloud Training (Google)
Develops practical skills in how to apply diffusion models to generate images
Assumes prior knowledge in Python programming, basic ML, and CNNs
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