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Sharon Zhou,
Eda Zhou,
and
Eric Zelikman
In this course, you will:
- Learn about GANs and their applications
- Understand the intuition behind the fundamental components of GANs
- Explore and implement multiple GAN architectures
- Build conditional GANs capable of generating examples from determined categories
Read more
In this course, you will:
- Learn about GANs and their applications
- Understand the intuition behind the fundamental components of GANs
- Explore and implement multiple GAN architectures
- Build conditional GANs capable of generating examples from determined categories
Read more
In this course, you will:
- Learn about GANs and their applications
- Understand the intuition behind the fundamental components of GANs
- Explore and implement multiple GAN architectures
- Build conditional GANs capable of generating examples from determined categories
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/wdzacy/build
What's inside
Syllabus
Week 1: Intro to GANs
See some real-world applications of GANs, learn about their fundamental components, and build your very own GAN using PyTorch!
Read more
Syllabus
Week 1: Intro to GANs
See some real-world applications of GANs, learn about their fundamental components, and build your very own GAN using PyTorch!
Read more
Week 2: Deep Convolutional GANs
Learn about different activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images!
Week 3: Wasserstein GANs with Gradient Penalty
Learn advanced techniques to reduce instances of GAN failure due to imbalances between the generator and discriminator! Implement a WGAN to mitigate unstable training and mode collapse using W-Loss and Lipschitz Continuity enforcement.
Week 4: Conditional GAN & Controllable Generation
Understand how to effectively control your GAN, modify the features in a generated image, and build conditional GANs capable of generating examples from determined categories!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces image generation with GANs, using an easy-to-understand approach
Provides hands-on experience in GANs, using PyTorch
Builds a comprehensive knowledge base in GANs
Covers social implications of GANs, including bias detection and privacy preservation
Designed for learners of all levels, including those without prior familiarity with advanced math and machine learning research
Taught by instructors who are recognized for their work in GANs
Save this course
Save Build Basic Generative Adversarial Networks (GANs) to your list so you can find it easily later:
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Reviews summary
Essential gans
Learners say this course provides a good introduction to the basics of Generative Adversarial Networks (GANs). Designed for learners with little to no prior GAN knowledge, the course focuses on building a foundational understanding of GANs, their components, and applications. Sharon Zhou’s lectures are highly praised for being clear, concise, engaging, and well-organized. The course materials include video lectures, interactive Jupyter notebooks, programming assignments, quizzes, and readings from research papers. Many learners appreciate the balance between theoretical explanations and practical implementation, as well as the inclusion of optional advanced materials for those who want to delve deeper. However, some learners also suggest that the assignments could be more challenging to provide a more effective learning experience.
Suitable for learners with little to no prior GAN knowledge.
"Sharon rocks! Very clear explanation of quite complicated material makes it relatively easy to understand GANs."
"The programming assignments can be improved by designing it in such a way that most of the work should be done learner not by the course designer. I hope you change it in future."
The course includes optional advanced readings and materials for further exploration.
"This course has been long waited for! It is great addition to the AI community and it presented very clearly. A bit of more theoretical background could be helpful."
"I have been trying to understand and implement GANs for que a few weeks and it felt really hard but after this course made everything easy for me, deeplearning.ai has been really one of the best places to learn."
Interactive Jupyter notebooks and programming assignments enhance the learning experience.
"Great explanation and great way to summarize huge topics but the assignments are really taking a huge time for training purpose if possible try to reduce the no.of epochs or provide a pre trained model and training the last layer"
"I very much enjoyed this course. There are three points that I want to point out about this course:1) The lecture is simple, but well organized.2) The code examples/assignments are simple, but provoking more thoughts.3) The Slack channel is really useful when you struggle."
Sharon Zhou's lectures are easy to understand and well-organized.
"Sharon Zhou did a great job explaining hard concepts."
"Sharon Nailed it on the insights and the intutions behind every concept discussed and their visual and crisp clarity reasonings."
Some learners suggest that the programming assignments could be more challenging.
"The programming assignments are pretty weak in difficulty level, could have had less hand holding there. Excited to get into more high resolution GANs soon!"
"There should be some explanation of the assignment's code. The lectures were precise and intresting. I like it. It was informative."
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 Basic Generative Adversarial Networks (GANs) with these
activities:
Review Fundamental Machine Learning Concepts
Show steps
Strengthen your understanding of fundamental machine learning concepts, such as supervised learning, unsupervised learning, and optimization, to enhance your grasp of GANs.
Browse courses on
Machine Learning
Show steps
-
Review lecture notes or online resources
-
Solve practice problems
-
Take a quiz or assessment
Read 'Generative Adversarial Networks: An Overview'
Show steps
Learn the core concepts of Generative Adversarial Networks (GANs), including their architecture, training process, and applications.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
-
Read Chapter 1: Introduction
-
Read Chapter 2: The GAN Objective
-
Read Chapter 3: Training GANs
Join a GAN Study Group
Show steps
Engage with peers and discuss GANs, share ideas, and learn from the collective knowledge of the group.
Browse courses on
GANs
Show steps
-
Find a study group or create your own
-
Set regular meeting times
-
Prepare topics for discussion
-
Participate actively in discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Coursera's 'GANs in PyTorch' Tutorial
Show steps
Gain hands-on experience building GANs using PyTorch by following a structured tutorial.
Browse courses on
GANs
Show steps
-
Set up your development environment
-
Implement a basic GAN
-
Train and evaluate your GAN
Write a Blog Post on a GAN Application
Show steps
Enhance your understanding of GANs by explaining their applications in a blog post, such as image generation or data augmentation.
Browse courses on
GANs
Show steps
-
Choose a specific GAN application
-
Research and gather information
-
Write a draft
-
Edit and revise your post
Solve GAN-related Coding Challenges
Show steps
Test your understanding of GANs by solving coding challenges on platforms like LeetCode or HackerRank.
Browse courses on
GANs
Show steps
-
Identify a coding challenge platform
-
Select a set of GAN-related challenges
-
Solve the challenges
-
Review your solutions
Contribute to an Open-Source GAN Project
Show steps
Gain practical experience and contribute to the GAN community by contributing to an open-source project.
Browse courses on
GANs
Show steps
-
Identify an open-source GAN project
-
Review the project's documentation
-
Identify a bug or feature to work on
-
Submit a pull request
Build a GAN for Image Generation
Show steps
Apply your knowledge of GANs by building a project that generates realistic images.
Browse courses on
GANs
Show steps
-
Choose a dataset of images
-
Design and implement the GAN architecture
-
Train and evaluate the GAN
-
Generate and explore the generated images
Review Fundamental Machine Learning Concepts
Show steps
Strengthen your understanding of fundamental machine learning concepts, such as supervised learning, unsupervised learning, and optimization, to enhance your grasp of GANs.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review lecture notes or online resources
- Solve practice problems
- Take a quiz or assessment
Read 'Generative Adversarial Networks: An Overview'
Show steps
Learn the core concepts of Generative Adversarial Networks (GANs), including their architecture, training process, and applications.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Introduction
- Read Chapter 2: The GAN Objective
- Read Chapter 3: Training GANs
Join a GAN Study Group
Show steps
Engage with peers and discuss GANs, share ideas, and learn from the collective knowledge of the group.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Find a study group or create your own
- Set regular meeting times
- Prepare topics for discussion
- Participate actively in discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Coursera's 'GANs in PyTorch' Tutorial
Show steps
Gain hands-on experience building GANs using PyTorch by following a structured tutorial.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Set up your development environment
- Implement a basic GAN
- Train and evaluate your GAN
Write a Blog Post on a GAN Application
Show steps
Enhance your understanding of GANs by explaining their applications in a blog post, such as image generation or data augmentation.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Choose a specific GAN application
- Research and gather information
- Write a draft
- Edit and revise your post
Solve GAN-related Coding Challenges
Show steps
Test your understanding of GANs by solving coding challenges on platforms like LeetCode or HackerRank.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Identify a coding challenge platform
- Select a set of GAN-related challenges
- Solve the challenges
- Review your solutions
Contribute to an Open-Source GAN Project
Show steps
Gain practical experience and contribute to the GAN community by contributing to an open-source project.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Identify an open-source GAN project
- Review the project's documentation
- Identify a bug or feature to work on
- Submit a pull request
Build a GAN for Image Generation
Show steps
Apply your knowledge of GANs by building a project that generates realistic images.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Choose a dataset of images
- Design and implement the GAN architecture
- Train and evaluate the GAN
- Generate and explore the generated images
Career center
Learners who complete Build Basic Generative Adversarial Networks (GANs) will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a software engineer who specializes in the design, development, and deployment of machine learning models. Machine Learning Engineers work on a variety of tasks, including data preparation, model training, and model evaluation. This course provides a strong foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Machine Learning Engineers can develop and apply GANs to solve real-world problems and drive innovation within their organizations.
Data Scientist
Data Scientist
A Data Scientist is a professional that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. Data Scientists play a crucial role in helping organizations make data-driven decisions to improve their operations and achieve their goals. This course provides a solid foundation in the fundamentals of GANs, which are powerful generative models that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Data Scientists can develop and apply GANs to solve real-world problems and drive innovation within their organizations.
Research Scientist
Research Scientist
A Research Scientist is a scientist who conducts research in a specific field of science. Research Scientists may work in academia, industry, or government. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Research Scientists can develop and apply GANs to their research projects and make significant contributions to their field.
Software Engineer
Software Engineer
A Software Engineer is a professional who designs, develops, and maintains software applications. Software Engineers work on a variety of projects, including web applications, mobile applications, and desktop applications. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Software Engineers can develop and apply GANs to create innovative software applications that solve real-world problems.
Data Analyst
Data Analyst
A Data Analyst is a professional who uses data to solve problems and make decisions. Data Analysts work with data from a variety of sources, including structured data, unstructured data, and big data. This course provides a strong foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Data Analysts can develop and apply GANs to solve real-world problems and drive innovation within their organizations.
Product Manager
Product Manager
A Product Manager is a professional who is responsible for the development and management of a product. Product Managers work with a variety of stakeholders, including engineers, designers, and marketers, to bring a product to market. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Product Managers can develop and apply GANs to create innovative products that meet the needs of their customers.
Business Analyst
Business Analyst
A Business Analyst is a professional who helps organizations improve their business processes. Business Analysts work with stakeholders to identify and analyze business problems and develop solutions. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Business Analysts can develop and apply GANs to solve real-world business problems and drive innovation within their organizations.
Marketing Manager
Marketing Manager
A Marketing Manager is a professional who is responsible for the development and implementation of marketing campaigns. Marketing Managers work with a variety of stakeholders, including customers, partners, and the media, to promote a product or service. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Marketing Managers can develop and apply GANs to create innovative marketing campaigns that reach their target audience and drive sales.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze financial data. Quantitative Analysts work with a variety of stakeholders, including investors, companies, and government agencies. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Quantitative Analysts can develop and apply GANs to analyze financial data and develop more sophisticated trading strategies.
Financial Analyst
Financial Analyst
A Financial Analyst is a professional who analyzes financial data to make investment recommendations. Financial Analysts work with a variety of stakeholders, including investors, companies, and government agencies. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Financial Analysts can develop and apply GANs to analyze financial data and make more informed investment recommendations.
Data Architect
Data Architect
A Data Architect is a professional who designs and builds data systems. Data Architects work with a variety of stakeholders, including data engineers, data scientists, and business analysts. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Data Architects can develop and apply GANs to design and build more efficient and effective data systems.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is a professional who uses mathematical and analytical techniques to solve business problems. Operations Research Analysts work with a variety of stakeholders, including businesses, government agencies, and non-profit organizations. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Operations Research Analysts can develop and apply GANs to solve real-world business problems and drive innovation within their organizations.
Systems Analyst
Systems Analyst
A Systems Analyst is a professional who analyzes and designs business systems. Systems Analysts work with a variety of stakeholders, including businesses, government agencies, and non-profit organizations. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Systems Analysts can develop and apply GANs to analyze and design more efficient and effective business systems.
Software Architect
Software Architect
A Software Architect is a professional who designs and builds software systems. Software Architects work with a variety of stakeholders, including software engineers, product managers, and business analysts. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Software Architects can develop and apply GANs to design and build more innovative and effective software systems.
Management Consultant
Management Consultant
A Management Consultant is a professional who helps organizations improve their performance. Management Consultants work with a variety of stakeholders, including businesses, government agencies, and non-profit organizations. This course provides a solid foundation in the fundamentals of GANs, which are a type of generative model that can be used to create new data from existing data. By understanding the concepts and techniques covered in this course, Management Consultants can develop and apply GANs to solve real-world business problems and drive innovation within their organizations.
For more career information including salaries, visit:
OpenCourser.com/course/wdzacy/build
Reading list
We've selected 13 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 Basic Generative Adversarial Networks (GANs).
Save
Provides a comprehensive overview of GANs, including their history, theory, and applications. It valuable resource for anyone who wants to learn more about GANs.
Save
Provides a comprehensive overview of deep learning, including its history, theory, and applications. It valuable resource for anyone who wants to learn more about deep learning.
Provides a hands-on guide to machine learning, including how to use popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone who wants to learn more about machine learning.
Provides an introduction to linear algebra, including its applications to machine learning. It valuable resource for anyone who wants to learn more about linear algebra.
Provides an introduction to convex optimization, including its applications to machine learning. It valuable resource for anyone who wants to learn more about convex optimization.
Provides an introduction to information theory, inference, and learning algorithms. It valuable resource for anyone who wants to learn more about these topics.
Provides a probabilistic perspective on machine learning. It valuable resource for anyone who wants to learn more about machine learning.
Provides an introduction to reinforcement learning, including its history, theory, and applications. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a hands-on guide to deep learning using Python. It valuable resource for anyone who wants to learn more about deep learning.
Provides a hands-on guide to machine learning using R. It valuable resource for anyone who wants to learn more about machine learning.
Provides a hands-on guide to machine learning using Python. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of machine learning algorithms. It valuable resource for anyone who wants to learn more about machine learning.
Provides a hands-on guide to machine learning for hackers. It valuable resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wdzacy/build
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Effort
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Sharon Zhou
Eda Zhou
Eric Zelikman
Language
English
Transcripts
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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)
- Build Basic Generative Adversarial Networks (GANs) (viewing)
- Apply Generative Adversarial Networks (GANs)
Good to know
Introduces image generation with GANs, using an easy-to-understand approach
Provides hands-on experience in GANs, using PyTorch
Builds a comprehensive knowledge base in GANs
Covers social implications of GANs, including bias detection and privacy preservation
Designed for learners of all levels, including those without prior familiarity with advanced math and machine learning research
Taught by instructors who are recognized for their work in GANs
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