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Build Basic Generative Adversarial Networks (GANs)

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

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.

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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!

Traffic lights

Read about what's good

what should give you pause

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

Create your own learning path. Save this course to your list so you can find it easily later.

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Reviews summary

Hands-on gans introduction with pytorch

According to learners, this course offers a solid introduction to Generative Adversarial Networks, widely praised for its clear explanations of complex topics, including underlying mathematical concepts. Students particularly value the hands-on projects and PyTorch implementations, which are noted to solidify understanding and provide practical experience. While considered highly effective for beginners and those aiming to apply GANs to their projects, some learners suggest it primarily provides foundational depth and could potentially explore more advanced architectures. The well-structured modules are consistently highlighted for their progressive and logical learning path.

Organized weekly modules build concepts progressively.

"The structure was great; week by week, it built up concepts nicely and logically."

"I appreciated the logical progression of topics from basics to conditional GANs, which made learning smooth."

"Each module was well-paced and clearly defined, making it easy to follow along."

Excellent starting point for GANs, even for beginners.

"It's an excellent introduction to GANs for anyone new to the field."

"I found it an accessible pathway to understanding GANs without prior advanced math, just as advertised."

"This was a perfect first step into the exciting world of generative models for me."

Offers hands-on PyTorch coding and practical applications.

"Loved the PyTorch implementations and the hands-on projects; they really solidified my understanding."

"I gained practical experience building GANs with PyTorch, which was invaluable for my portfolio."

"The course was very practical; I could apply the concepts immediately to my own generative AI projects."

Simplifies complex GAN concepts effectively.

"The explanations were super clear, especially for the math behind it; I finally understood it."

"I found the instructor explained complex topics well, especially the mode collapse part, which was very helpful."

"This course demystifies GANs for me, even without a strong machine learning background, making it accessible."

Covers foundational GAN architectures, but may lack advanced topics.

"While a good foundation, I felt it could go deeper into more advanced architectures or real-world use cases."

"I was hoping for more advanced techniques, but it's really good for beginners who need the basics first."

"Some topics felt a bit basic to me; I wish there was more depth in certain areas for intermediate learners."

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

Career center

Learners who complete Build Basic Generative Adversarial Networks (GANs) will develop knowledge and skills that may be useful to these careers:

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Research Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Marketing Manager

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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Management Consultant

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Software Architect

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.

See salaries and explore the career path for Systems Analyst

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).

Deep Learning

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.

Deep Learning (Adaptive Computation and Machine...

Hardcover

$$$

Deep Learning (Adaptive Computation and Machine...

Kindle Edition

$$$

Deep Learning

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.

Deep Learning

Paperback

$$$

Deep Learning (Adaptive Computation and Machine...

Hardcover

$$$

Deep Learning (Adaptive Computation and Machine...

Kindle Edition

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

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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.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Paperback

Hands-On Machine Learning with Scikit-Learn, Keras,...

Paperback

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

Kindle Edition

Introduction to Applied Linear Algebra

Save

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.

Introduction to Applied Linear Algebra: Vectors,...

Hardcover

Introduction to Applied Linear Algebra: Vectors,...

Kindle Edition

Introduction to Applied Linear Algebra

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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.

Introduction to Applied Linear Algebra: Vectors,...

Hardcover

Introduction to Applied Linear Algebra: Vectors,...

Kindle Edition

Information Theory, Inference and Learning...

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Provides an introduction to information theory, inference, and learning algorithms. It valuable resource for anyone who wants to learn more about these topics.

Information Theory, Inference and Learning...

Hardcover

$$$

Probabilistic Machine Learning

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Provides a probabilistic perspective on machine learning. It valuable resource for anyone who wants to learn more about machine learning.

Probabilistic Machine Learning: Advanced Topics...

Hardcover

$$$$

Reinforcement Learning, second edition

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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.

Reinforcement Learning, second edition: An...

Hardcover

$$$

Reinforcement Learning, second edition: An...

Kindle Edition

$$$

Deep Learning with Python, Second Edition

Save

Provides a hands-on guide to deep learning using Python. It valuable resource for anyone who wants to learn more about deep learning.

Deep Learning with Python, Second Edition

Paperback

Deep Learning with Python, Second Edition

Kindle Edition

Machine Learning with R

Save

Provides a hands-on guide to machine learning using R. It valuable resource for anyone who wants to learn more about machine learning.

Machine Learning with R - Fourth Edition: Learn...

Paperback

Machine Learning with R - Second Edition: Expert...

Kindle Edition

Machine Learning with R - Third Edition: Expert...

Paperback

(Italiano) Machine Learning con R: Conoscere le tecniche per...

Kindle Edition

R: Unleash Machine Learning Techniques

Kindle Edition

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Machine Learning with R: Learn How to Use R to...

Paperback

(中文) Machine learning and R language (3rd edition of the...

Paperback

Machine Learning in Action

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Provides a hands-on guide to machine learning using Python. It valuable resource for anyone who wants to learn more about machine learning.

Machine Learning in Action

Paperback

(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)

Kindle Edition

Mastering Machine Learning Algorithms

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Provides a comprehensive overview of machine learning algorithms. It valuable resource for anyone who wants to learn more about machine learning.

Mastering Machine Learning Algorithms - Second...

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Mastering Machine Learning Algorithms: Expert...

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Mastering Machine Learning Algorithms: Expert...

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Mastering Machine Learning Algorithms: Expert...

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Machine Learning for Hackers

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Provides a hands-on guide to machine learning for hackers. It valuable resource for anyone who wants to learn more about machine learning.

Machine Learning for Hackers: Case Studies and...

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Machine Learning for Hackers: Case Studies and...

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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

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)
Build Basic Generative Adversarial Networks (GANs) (viewing)
Apply Generative Adversarial Networks (GANs)

Traffic lights

Read about what's good

what should give you pause

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

Build Basic Generative Adversarial Networks (GANs)

Here's a deal for you

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Hands-on gans introduction with pytorch

Activities

Career center

Reading list

Share

Similar courses

Coursera Buyer Protection & Guarantee

7-Day Free Trial & Free Audit

14-Day Refund Guarantee

Verified Academic & Industry Partner