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Brian Cruz,
Emily McMilin,
Victor Geislinger,
Jason Lin,
Erick Galinkin,
Giacomo Vianello,
Chuyi Shang,
Annabel Ng,
Derek Xu,
Nathaniel Haynam,
Valerie Scarlata,
Chang She,
and
Sergei Kozyrenko
Learn cutting edge AI skills and unlock AI's visual power with Udacity's Computer Vision & Generative AI Training Course. Enroll to become an AI innovator!
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:
- Transformer neural networks
- Hugging Face
- Deep learning
- Prompt Engineering
- PyTorch
- Foundation Models
- Generative AI Fluency
- Intermediate Python
Read more
Learn cutting edge AI skills and unlock AI's visual power with Udacity's Computer Vision & Generative AI Training Course. Enroll to become an AI innovator!
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:
- Transformer neural networks
- Hugging Face
- Deep learning
- Prompt Engineering
- PyTorch
- Foundation Models
- Generative AI Fluency
- Intermediate Python
Read more
Learn cutting edge AI skills and unlock AI's visual power with Udacity's Computer Vision & Generative AI Training Course. Enroll to become an AI innovator!
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:
- Transformer neural networks
- Hugging Face
- Deep learning
- Prompt Engineering
- PyTorch
- Foundation Models
- Generative AI Fluency
- Intermediate Python
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/yl99am/computer
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What's inside
Syllabus
In this lesson, you will define image generation and understand its relevance in AI and machine learning.
Learn how computers see images and perform key image processing techniques using classic image processing techniques such as image transformation, noise reduction, and more.
Read more
Syllabus
In this lesson, you will define image generation and understand its relevance in AI and machine learning.
Learn how computers see images and perform key image processing techniques using classic image processing techniques such as image transformation, noise reduction, and more.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Taught by recognized experts in the field of Computer Vision and Generative AI, including Brian Cruz, Emily McMilin, Victor Geislinger, and more
Covers the latest advancements in image generation, generative adversarial networks, and diffusion algorithms
Provides hands-on experience with Hugging Face Diffusers, a state-of-the-art library for image generation
Develops a strong foundation in Generative AI, which is increasingly used in various industries
Requires prerequisites in deep learning, transformer neural networks, and prompt engineering
Assumes some background knowledge in image processing techniques
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Reviews summary
Advanced computer vision and generative ai
Learners say this course offers a solid and practical deep dive into Computer Vision and Generative AI. Students particularly praise its hands-on approach, especially the practical application of Stable Diffusion for image manipulation and Hugging Face Diffusers. The coverage of Vision Transformers like DALL-E and SAM is highly valued for its currency. However, a significant number of reviews highlight that the prerequisites are substantial, requiring more than intermediate Python; prior fluency in PyTorch, Foundation Models, and general Deep Learning is critical, as the pace can be relentless. Some also note that the initial classic image processing content feels rushed or less relevant to the cutting-edge focus.
Covers the latest and relevant generative AI models effectively.
"The section on Vision Transformers and their architecture was particularly insightful, especially the practical insights into DALL-E and SAM."
"A solid overview of modern CV and GenAI. The strongest points were the up-to-date coverage of models."
"Good content, particularly the deep dive into diffusion algorithms."
Focuses on hands-on application with real-world tools is a strength.
"The hands-on project using Stable Diffusion to swap backgrounds was a great application of the concepts."
"The Hugging Face Diffusers practicals were invaluable."
"The hands-on exercises were well-designed, and the project was challenging but rewarding. It truly delivers on its promise of modern AI skills."
"I appreciated the emphasis on practical application over pure theory."
Varied opinions on content depth and overall course flow.
"The course felt like a collection of disparate topics rather than a cohesive learning journey."
"The lectures often lacked sufficient depth for a complete understanding without significant external research."
"The explanations could be more detailed, and some parts felt like they were skipping over crucial theoretical underpinnings."
"For me, it perfectly bridged the gap between theoretical understanding and practical application of generative AI."
Initial content's relevance debated by learners.
"I felt some of the initial classic image processing felt a bit rushed given the advanced topic, and assumed more prior knowledge."
"The course claims to teach 'cutting edge AI skills' but some parts, particularly the classic image processing, felt outdated."
"I found the initial module on classic image processing to be somewhat out of place for a course focusing on 'cutting edge' GenAI."
"Though some considered it good foundational context, others felt it didn't align with the advanced focus."
Requires deep existing knowledge beyond stated prerequisites.
"The pace for topics like PyTorch and Foundation Models was very fast, almost like a refresher rather than a teaching module."
"It's definitely for experienced practitioners, not just intermediate Python users."
"The prerequisite knowledge was vastly understated. I came in with intermediate Python but felt completely lost."
"I found myself struggling with the sheer volume of new concepts introduced in a relatively short time, even with all prerequisites."
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 Computer Vision and Generative AI with these
activities:
Prompt Engineering Exercises
Show steps
Enhance your proficiency in prompt engineering, enabling you to craft effective prompts that yield exceptional image generation results.
Browse courses on
Prompt Engineering
Show steps
-
Practice writing clear and concise prompts.
-
Experiment with different prompt elements.
-
Iterate and refine your prompts based on feedback.
Image Generation: Artistic Exploration
Show steps
Foster your creativity and explore the artistic possibilities of image generation, expanding your horizons and inspiring novel artistic approaches.
Browse courses on
Image Generation
Show steps
-
Experiment with different prompts and generation settings.
-
Create a collection of visually striking images.
-
Share your creations and engage with the community.
Walkthrough: Vision Transformers for Image Generation
Show steps
Build a strong conceptual framework for vision transformers, empowering you to confidently navigate complex image generation tasks.
Show steps
-
Explore the fundamentals of Vision Transformers.
-
Delve into the architecture of DALL-E.
-
Practice implementing Vision Transformers for image generation.
One other activity
Expand to see all activities and additional details
Show all four activities
Diffusion Model Implementation
Show steps
Solidify your understanding of diffusion models by implementing your own from scratch, enhancing your ability to generate and manipulate images.
Browse courses on
Diffusion Models
Show steps
-
Gain a theoretical understanding of diffusion models.
-
Implement a diffusion model using Hugging Face Diffusers.
-
Generate images using your implemented model.
Prompt Engineering Exercises
Show steps
Enhance your proficiency in prompt engineering, enabling you to craft effective prompts that yield exceptional image generation results.
Browse courses on
Prompt Engineering
Show steps
Show steps
Show steps
- Practice writing clear and concise prompts.
- Experiment with different prompt elements.
- Iterate and refine your prompts based on feedback.
Image Generation: Artistic Exploration
Show steps
Foster your creativity and explore the artistic possibilities of image generation, expanding your horizons and inspiring novel artistic approaches.
Browse courses on
Image Generation
Show steps
Show steps
Show steps
- Experiment with different prompts and generation settings.
- Create a collection of visually striking images.
- Share your creations and engage with the community.
Walkthrough: Vision Transformers for Image Generation
Show steps
Build a strong conceptual framework for vision transformers, empowering you to confidently navigate complex image generation tasks.
Show steps
Show steps
Show steps
- Explore the fundamentals of Vision Transformers.
- Delve into the architecture of DALL-E.
- Practice implementing Vision Transformers for image generation.
One other activity
Expand to see all activities and additional details
Show all four activities
Diffusion Model Implementation
Show steps
Solidify your understanding of diffusion models by implementing your own from scratch, enhancing your ability to generate and manipulate images.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Gain a theoretical understanding of diffusion models.
- Implement a diffusion model using Hugging Face Diffusers.
- Generate images using your implemented model.
Career center
Learners who complete Computer Vision and Generative AI will develop knowledge and skills
that may be useful to these careers:
Generative AI Engineer
Generative AI Engineer
A Generative AI Engineer develops and deploys generative AI models. The Computer Vision and Generative AI course can provide a strong foundation for a career in Generative AI Engineering by teaching students how to use AI to generate images, text, and other types of data. This course covers techniques such as generative adversarial networks, transformers, and diffusion models, which are essential skills for Generative AI Engineers who work with visual data. Additionally, the course's focus on Computer Vision can help Generative AI Engineers develop new methods for generating and manipulating images.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer develops and deploys computer vision systems. The Computer Vision and Generative AI course can provide a strong foundation for a career in Computer Vision Engineering by teaching students how to use AI to process and analyze images. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Computer Vision Engineers who work with visual data. Additionally, the course's focus on Generative AI can help Computer Vision Engineers develop new methods for generating and manipulating images.
AI Researcher
AI Researcher
An AI Researcher develops new AI algorithms and techniques. The Computer Vision and Generative AI course can provide a strong foundation for a career in AI Research by teaching students how to use AI to process and analyze images. This course covers techniques such as deep learning, reinforcement learning, and generative adversarial networks, which are essential skills for AI Researchers who work with visual data. Additionally, the course's focus on Generative AI can help AI Researchers develop new methods for generating and manipulating images.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and deploys machine learning models. The Computer Vision and Generative AI course can provide a strong foundation for a career in Machine Learning Engineering by teaching students how to use AI to create models that can recognize and generate images. This course covers techniques such as convolutional neural networks, generative adversarial networks, and transformers, which are essential skills for Machine Learning Engineers who work with visual data.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and deploys robots. The Computer Vision and Generative AI course can provide a strong foundation for a career in Robotics Engineering by teaching students how to use AI to develop robots that can see and interact with the world around them. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Robotics Engineers who work with visual data. Additionally, the course's focus on Generative AI can help Robotics Engineers develop new methods for generating and manipulating data to train robots.
Animator
Animator
An Animator creates animated content for a variety of purposes, such as film, television, and video games. The Computer Vision and Generative AI course can provide a strong foundation for a career in Animation by teaching students how to use AI to create realistic and engaging animations. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Animators who work with visual data. Additionally, the course's focus on Generative AI can help Animators develop new methods for generating and manipulating animations.
Game Developer
Game Developer
A Game Developer designs and develops video games. The Computer Vision and Generative AI course can provide a strong foundation for a career in Game Development by teaching students how to use AI to create visually appealing and engaging games. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Game Developers who work with visual data. Additionally, the course's focus on Generative AI can help Game Developers develop new methods for generating and manipulating game content.
Data Scientist
Data Scientist
A Data Scientist analyzes data to extract meaningful insights and make predictions. The Computer Vision and Generative AI course can provide a strong foundation for a career in Data Science by teaching students how to use AI to process and analyze images. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Data Scientists who work with visual data. Additionally, the course's focus on Generative AI can help Data Scientists develop new methods for generating and manipulating data.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to identify trends and patterns. The Computer Vision and Generative AI course can provide a strong foundation for a career in Data Analysis by teaching students how to use AI to process and analyze images. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Data Analysts who work with visual data. Additionally, the course's focus on Generative AI can help Data Analysts develop new methods for generating and manipulating images.
Graphic Designer
Graphic Designer
A Graphic Designer creates visual content for a variety of purposes, such as marketing, advertising, and web design. The Computer Vision and Generative AI course can provide a strong foundation for a career in Graphic Design by teaching students how to use AI to create visually appealing and engaging content. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Graphic Designers who work with visual data. Additionally, the course's focus on Generative AI can help Graphic Designers develop new methods for generating and manipulating images.
Photographer
Photographer
A Photographer captures images for a variety of purposes, such as art, journalism, and commercial photography. The Computer Vision and Generative AI course can provide a strong foundation for a career in Photography by teaching students how to use AI to enhance and manipulate images. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Photographers who work with visual data. Additionally, the course's focus on Generative AI can help Photographers develop new methods for generating and manipulating images.
Videographer
Videographer
A Videographer captures and edits video footage for a variety of purposes, such as film, television, and commercials. The Computer Vision and Generative AI course can provide a strong foundation for a career in Videography by teaching students how to use AI to enhance and manipulate video footage. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Videographers who work with visual data. Additionally, the course's focus on Generative AI can help Videographers develop new methods for generating and manipulating video footage.
Software Engineer
Software Engineer
A Software Engineer designs, builds, and deploys software applications. The Computer Vision and Generative AI course can provide a strong foundation for a career in Software Engineering by teaching students how to use AI to develop software applications that can process and analyze images. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Software Engineers who work with visual data. Additionally, the course's focus on Generative AI can help Software Engineers develop new methods for generating and manipulating images.
UX Designer
UX Designer
A UX Designer designs user interfaces for software products. The Computer Vision and Generative AI course can provide a strong foundation for a career in UX Design by teaching students how to use AI to design user interfaces that are visually appealing and easy to use. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for UX Designers who work with visual data. Additionally, the course's focus on Generative AI can help UX Designers develop new methods for generating and manipulating images.
Product Manager
Product Manager
A Product Manager develops and manages software products. The Computer Vision and Generative AI course can provide a strong foundation for a career in Product Management by teaching students how to use AI to develop software products that can process and analyze images. This course covers techniques such as image transformation, noise reduction, and segmentation, which are essential skills for Product Managers who work with visual data. Additionally, the course's focus on Generative AI can help Product Managers develop new methods for generating and manipulating images.
For more career information including salaries, visit:
OpenCourser.com/course/yl99am/computer
Reading list
We've selected seven 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
Computer Vision and Generative AI.
Save
Seminal work on GANs and provides a deep dive into the theory and practice of these models.
This research paper introduces GANs and explores their applications in image generation.
Provides a practical introduction to deep learning for computer vision, covering topics such as convolutional neural networks, object detection, and image segmentation.
Great resource for deep learning fundamentals and provides a solid basis for understanding the concepts covered in the Computer Vision and Generative AI course.
Provides a comprehensive overview of computer vision algorithms and applications, covering topics such as image formation, feature detection, object recognition, and image segmentation.
Provides a comprehensive and up-to-date overview of computer vision, covering topics such as image formation, feature detection, object recognition, and image segmentation.
Save
Provides a comprehensive overview of computer vision algorithms and applications, covering topics such as image formation, feature detection, object recognition, and image segmentation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yl99am/computer
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Effort
4 weeks
Level
Intermediate
Via
Udacity
Instructors
Brian Cruz
Emily McMilin
Victor Geislinger
Jason Lin
Erick Galinkin
Giacomo Vianello
Chuyi Shang
Annabel Ng
Derek Xu
Nathaniel Haynam
Valerie Scarlata
Chang She
Sergei Kozyrenko
Language
English
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