We may earn an affiliate commission when you visit our partners.
Sharon Zhou,
Eda Zhou,
and
Eric Zelikman
In this course, you will:
- Assess the challenges of evaluating GANs and compare different generative models
- Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
- Identify sources of bias and the ways to detect it in GANs
- Learn and implement the techniques associated with the state-of-the-art StyleGANs
Read more
In this course, you will:
- Assess the challenges of evaluating GANs and compare different generative models
- Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
- Identify sources of bias and the ways to detect it in GANs
- Learn and implement the techniques associated with the state-of-the-art StyleGANs
Read more
In this course, you will:
- Assess the challenges of evaluating GANs and compare different generative models
- Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
- Identify sources of bias and the ways to detect it in GANs
- Learn and implement the techniques associated with the state-of-the-art StyleGANs
The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.
This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.
Register for this course and see more details by visiting:
OpenCourser.com/course/ree7t9/build
What's inside
Syllabus
Week 1: Evaluation of GANs
Understand the challenges of evaluating GANs, learn about the advantages and disadvantages of different GAN performance measures, and implement the Fréchet Inception Distance (FID) method using embeddings to assess the accuracy of GANs!
Read more
Syllabus
Week 1: Evaluation of GANs
Understand the challenges of evaluating GANs, learn about the advantages and disadvantages of different GAN performance measures, and implement the Fréchet Inception Distance (FID) method using embeddings to assess the accuracy of GANs!
Read more
Week 2: GAN Disadvantages and Bias
Learn the disadvantages of GANs when compared to other generative models, discover the pros/cons of these models—plus, learn about the many places where bias in machine learning can come from, why it’s important, and an approach to identify it in GANs!
Week 3: StyleGAN and Advancements
Learn how StyleGAN improves upon previous models and implement the components and the techniques associated with StyleGAN, currently the most state-of-the-art GAN with powerful capabilities!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a pathway for beginners and more experienced learners to navigate complex concepts in GANs
Covers common pitfalls and biases in GANs, empowering learners to create more accurate and diverse models
Incorporates StyleGAN, showcasing cutting-edge advancements in GAN technology
Instructor team comprises experts in the field of GANs, bringing valuable insights to learners
Provides hands-on experience in training and evaluating GANs, equipping learners with practical skills
Requires some familiarity with machine learning concepts, setting an appropriate level of expectation for learners
Save this course
Save Build Better Generative Adversarial Networks (GANs) to your list so you can find it easily later:
Save
Save
Reviews summary
Gan knowledge advancement
Students say that state-of-the-art GANs are covered in this course, including components like StyleGAN. There is a focus on ethics and fairness in AI, which some students say could be separated into a separate course. Assignments are said to help with practical understanding, though they can sometimes be difficult. The course is well-presented and contains helpful information, though some students mention that they would prefer more technical depth.
Assignments are helpful for understanding concepts.
"Assignments are said to help with practical understanding."
The course has a focus on ethics and fairness.
"There is a focus on ethics and fairness in AI."
Course helps to develop practical skills.
"The course is said to contain helpful information."
The course is well-taught.
"The course is said to be well-presented."
The course covers StyleGAN.
"Students say that state-of-the-art GANs are covered in this course, including components like StyleGAN."
The course lacks technical depth.
"Some students mention that they would prefer more technical depth."
Assignments can be challenging.
"Assignments are said to be difficult."
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 Build Better Generative Adversarial Networks (GANs) with these
activities:
Compile class materials into a study guide
Show steps
Organize and consolidate your class materials to facilitate effective review and retention.
Show steps
-
Review your class notes, handouts, and assignments.
-
Identify the key concepts and topics covered in class.
-
Create a structured study guide that outlines the key concepts and provides supporting materials.
Review linear algebra and probability theory
Show steps
Refresh your knowledge of linear algebra and probability theory to strengthen your understanding of GANs.
Browse courses on
Linear Algebra
Show steps
-
Review the basics of linear algebra, including matrices, vectors, and transformations.
-
Review the basics of probability theory, including probability distributions, random variables, and Bayes' theorem.
-
Apply your refreshed knowledge to understand the mathematical concepts underlying GANs.
Review machine learning concepts
Show steps
Refresh your understanding of machine learning concepts to enhance your comprehension of GANs.
Browse courses on
Machine Learning
Show steps
-
Review the different types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
-
Review the key concepts of supervised learning, such as classification and regression.
-
Review the key concepts of unsupervised learning, such as clustering and dimensionality reduction.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Introduction to Generative Adversarial Networks
Show steps
Improve your understanding of the theoretical foundations of GANs.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Summarize the key concepts of GANs.
-
Identify the challenges and limitations of GANs.
Implement the Fréchet Inception Distance (FID) method
Show steps
Gain hands-on experience in evaluating the performance of GANs using the FID method.
Show steps
-
Install the necessary libraries and dependencies.
-
Load a pre-trained GAN model.
-
Generate a dataset of real and synthetic images.
-
Calculate the FID score for the synthetic images.
-
Interpret the FID score and draw conclusions about the performance of the GAN model.
Build a GAN model using StyleGANs
Show steps
Deepen your understanding of GANs by building a model using the state-of-the-art StyleGAN architecture.
Show steps
-
Choose a dataset and prepare it for training.
-
Implement the StyleGAN architecture using PyTorch or TensorFlow.
-
Train the GAN model on the dataset.
-
Evaluate the performance of the GAN model.
-
Generate images using the trained GAN model.
Create a blog post or tutorial on GANs
Show steps
Enhance your understanding of GANs by explaining the concepts and techniques to others.
Browse courses on
GANs
Show steps
-
Choose a specific aspect of GANs to focus on.
-
Research the topic thoroughly.
-
Write a clear and concise blog post or tutorial.
-
Share your content with others online.
Contribute to an open-source GAN project
Show steps
Gain practical experience with GANs while contributing to the open-source community.
Browse courses on
Open Source
Show steps
-
Find an open-source GAN project that aligns with your interests.
-
Familiarize yourself with the project's codebase and documentation.
-
Identify an area where you can contribute.
-
Submit a pull request with your contributions.
-
Communicate with the project maintainers to improve your contributions.
Compile class materials into a study guide
Show steps
Organize and consolidate your class materials to facilitate effective review and retention.
Show steps
Show steps
Show steps
- Review your class notes, handouts, and assignments.
- Identify the key concepts and topics covered in class.
- Create a structured study guide that outlines the key concepts and provides supporting materials.
Review linear algebra and probability theory
Show steps
Refresh your knowledge of linear algebra and probability theory to strengthen your understanding of GANs.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of linear algebra, including matrices, vectors, and transformations.
- Review the basics of probability theory, including probability distributions, random variables, and Bayes' theorem.
- Apply your refreshed knowledge to understand the mathematical concepts underlying GANs.
Review machine learning concepts
Show steps
Refresh your understanding of machine learning concepts to enhance your comprehension of GANs.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review the different types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
- Review the key concepts of supervised learning, such as classification and regression.
- Review the key concepts of unsupervised learning, such as clustering and dimensionality reduction.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Introduction to Generative Adversarial Networks
Show steps
Improve your understanding of the theoretical foundations of GANs.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Summarize the key concepts of GANs.
- Identify the challenges and limitations of GANs.
Implement the Fréchet Inception Distance (FID) method
Show steps
Gain hands-on experience in evaluating the performance of GANs using the FID method.
Show steps
Show steps
Show steps
- Install the necessary libraries and dependencies.
- Load a pre-trained GAN model.
- Generate a dataset of real and synthetic images.
- Calculate the FID score for the synthetic images.
- Interpret the FID score and draw conclusions about the performance of the GAN model.
Build a GAN model using StyleGANs
Show steps
Deepen your understanding of GANs by building a model using the state-of-the-art StyleGAN architecture.
Show steps
Show steps
Show steps
- Choose a dataset and prepare it for training.
- Implement the StyleGAN architecture using PyTorch or TensorFlow.
- Train the GAN model on the dataset.
- Evaluate the performance of the GAN model.
- Generate images using the trained GAN model.
Create a blog post or tutorial on GANs
Show steps
Enhance your understanding of GANs by explaining the concepts and techniques to others.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Choose a specific aspect of GANs to focus on.
- Research the topic thoroughly.
- Write a clear and concise blog post or tutorial.
- Share your content with others online.
Contribute to an open-source GAN project
Show steps
Gain practical experience with GANs while contributing to the open-source community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source GAN project that aligns with your interests.
- Familiarize yourself with the project's codebase and documentation.
- Identify an area where you can contribute.
- Submit a pull request with your contributions.
- Communicate with the project maintainers to improve your contributions.
Career center
Learners who complete Build Better Generative Adversarial Networks (GANs) will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models for various applications, such as image recognition, natural language processing, and predictive analytics. This course will provide you with a solid foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of machine learning applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Machine Learning Engineer positions.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems and make predictions. They use a variety of techniques, including machine learning, to analyze data and extract insights. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of data science applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Data Scientist positions.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems, which are used to process and analyze images and videos. This course will provide you with a solid foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of computer vision applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Computer Vision Engineer positions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use a variety of programming languages and technologies to create software that meets the needs of users. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of software applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Software Engineer positions.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop AI systems, which are used to perform tasks that would normally require human intelligence. This course will provide you with a solid foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of AI applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Artificial Intelligence Engineer positions.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including machine learning, computer vision, and natural language processing. This course will provide you with a solid foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of research applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own research projects, which will make you a more competitive candidate for Research Scientist positions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of data analysis applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Data Analyst positions.
Business Analyst
Business Analyst
Business Analysts help businesses understand their needs and develop solutions to improve their operations. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of business analysis applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Business Analyst positions.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of product development applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own products, which will make you a more competitive candidate for Product Manager positions.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work with a variety of teams to create and implement marketing strategies that reach target audiences and achieve business goals. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of marketing applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own marketing campaigns, which will make you a more competitive candidate for Marketing Manager positions.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. They work with customers to identify their needs and develop solutions that meet those needs. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of sales applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own sales strategies, which will make you a more competitive candidate for Sales Manager positions.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. They work with customers to resolve issues, provide support, and ensure that customers are getting the most value from their products or services. This course will provide you with a strong foundation in the fundamentals of GANs, which are a powerful type of generative model used in a wide range of customer success applications. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own customer success strategies, which will make you a more competitive candidate for Customer Success Manager positions.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with a variety of stakeholders to ensure that projects are completed on time, within budget, and to the required quality standards. This course may be useful for Project Managers who are looking to use GANs in their projects. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own projects, which will make you a more competitive candidate for Project Manager positions.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. They work with a variety of teams to ensure that the business is running smoothly and efficiently. This course may be useful for Operations Managers who are looking to use GANs in their operations. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own operations, which will make you a more competitive candidate for Operations Manager positions.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts are responsible for ensuring that products or services meet quality standards. They work with a variety of teams to identify and resolve quality issues. This course may be useful for Quality Assurance Analysts who are looking to use GANs in their quality assurance processes. By taking this course, you will gain the skills and knowledge necessary to develop and deploy GANs for your own quality assurance processes, which will make you a more competitive candidate for Quality Assurance Analyst positions.
For more career information including salaries, visit:
OpenCourser.com/course/ree7t9/build
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
Build Better Generative Adversarial Networks (GANs).
Save
Provides a comprehensive overview of GANs, covering the theoretical foundations, different architectures, and applications. It valuable resource for anyone interested in learning more about GANs.
Save
Provides a comprehensive overview of deep learning, covering the theoretical foundations, different architectures, and applications. It valuable resource for anyone interested in learning more about deep learning.
Provides a comprehensive overview of artificial intelligence, covering the different subfields, algorithms, and applications. It valuable resource for anyone interested in learning more about artificial intelligence.
Provides a comprehensive overview of the mathematics used in machine learning, covering the different concepts, techniques, and applications. It valuable resource for anyone interested in learning more about the mathematics of machine learning.
Provides a comprehensive overview of the ethics of artificial intelligence, covering the different ethical issues raised by artificial intelligence, the different ethical frameworks used to address these issues, and the different ethical guidelines developed for the development and use of artificial intelligence. It valuable resource for anyone interested in learning more about the ethics of artificial intelligence.
Provides a comprehensive overview of deep learning with Python, covering the different architectures, techniques, and applications. It valuable resource for anyone interested in learning more about deep learning with Python.
Provides a comprehensive overview of TensorFlow for deep learning, covering the different architectures, techniques, and applications. It valuable resource for anyone interested in learning more about TensorFlow for deep learning.
Provides a comprehensive overview of PyTorch for deep learning, covering the different architectures, techniques, and applications. It valuable resource for anyone interested in learning more about PyTorch for deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ree7t9/build
Share
Similar courses
Here are nine courses similar to
Build Better Generative Adversarial Networks (GANs).
Build Basic Generative Adversarial Networks (GANs)
OpenCourser.com/course/wdzacy/build
Build Basic Generative Adversarial Networks (GANs)
Most relevant
Apply Generative Adversarial Networks (GANs)
OpenCourser.com/course/is4jpp/apply
Apply Generative Adversarial Networks (GANs)
Most relevant
Introduction to Generative AI
OpenCourser.com/course/cvp0l1/introduction
Introduction to Generative AI
Generative AI for Business - A Leaders' Handbook
OpenCourser.com/course/nz2a1u/generative
Generative AI for Business - A Leaders' Handbook
Building Generative Adversarial Networks
OpenCourser.com/course/wdp89a/building
Building Generative Adversarial Networks
Exploring Generative AI Models and Architecture
OpenCourser.com/course/l1q963/exploring
Exploring Generative AI Models and Architecture
Literacy Essentials: Core Concepts Generative Adversarial Network
OpenCourser.com/course/9y5k1i/literacy
Literacy Essentials: Core Concepts Generative Adversarial...
Building your first GAN in Python
OpenCourser.com/course/urqgxj/building
Building your first GAN in Python
Style Transfer with PyTorch
OpenCourser.com/course/p0gczd/style
Style Transfer with PyTorch
Effort
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Sharon Zhou
Eda Zhou
Eric Zelikman
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
tiếng Việt
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Generative Adversarial Networks (GANs). It's comprised of three courses:
- Build Better Generative Adversarial Networks (GANs) (viewing)
- Build Basic Generative Adversarial Networks (GANs)
- Apply Generative Adversarial Networks (GANs)
Good to know
Provides a pathway for beginners and more experienced learners to navigate complex concepts in GANs
Covers common pitfalls and biases in GANs, empowering learners to create more accurate and diverse models
Incorporates StyleGAN, showcasing cutting-edge advancements in GAN technology
Instructor team comprises experts in the field of GANs, bringing valuable insights to learners
Provides hands-on experience in training and evaluating GANs, equipping learners with practical skills
Requires some familiarity with machine learning concepts, setting an appropriate level of expectation for learners
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser