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Giacomo Vianello,
Nathan Klarer,
Erick Galinkin,
and
Thomas Hossler
Take Udacity's Introduction to Generative Adversarial Networks (GANs) course and learn how to build and train different GANs architectures to generate new images.
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:
- Jupyter notebooks
- PyTorch
- Basic calculus
- Variational autoencoders
- Python proficiency
- Linear algebra
- Fully convolutional neural networks
- Basic probability
Read more
Take Udacity's Introduction to Generative Adversarial Networks (GANs) course and learn how to build and train different GANs architectures to generate new images.
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:
- Jupyter notebooks
- PyTorch
- Basic calculus
- Variational autoencoders
- Python proficiency
- Linear algebra
- Fully convolutional neural networks
- Basic probability
Read more
Take Udacity's Introduction to Generative Adversarial Networks (GANs) course and learn how to build and train different GANs architectures to generate new images.
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:
- Jupyter notebooks
- PyTorch
- Basic calculus
- Variational autoencoders
- Python proficiency
- Linear algebra
- Fully convolutional neural networks
- Basic probability
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/wdp89a/building
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What's inside
Syllabus
Introduction to this course, prerequisites, and your course instructor.
Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You'll also implement your own GAN on the MNIST dataset.
Read more
Syllabus
Introduction to this course, prerequisites, and your course instructor.
Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You'll also implement your own GAN on the MNIST dataset.
Read more
In this lesson, you'll implement a Deep Convolution GAN to generate complex color images.
Jun-Yan Zhu, one of the creators of the CycleGAN, will lead you through Pix2Pix and CycleGAN formulations that learn to do image-to-image translation tasks.
In this lesson, you will implement more advanced GAN architectural techniques that have had a significant impact on the realism of generated images.
Define two adversarial networks, a generator, and a discriminator, and train them until you can generate realistic faces.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores GANs, which are increasingly used to generate images, videos, and audio data for a variety of purposes, such as creating realistic-looking avatars for virtual reality games or generating synthetic data for training self-driving cars
Taught by Giacomo Vianello, Nathan Klarer, Erick Galinkin, and Thomas Hossler, who are recognized experts in the field of deep learning and artificial intelligence
Covers the basics of GANs and then dives into more advanced topics, such as conditional GANs, GANs for image-to-image translation, and GANs for video generation
Requires students to have a strong foundation in deep learning, as well as experience with Python and PyTorch
Some of the prerequisite knowledge, such as variational autoencoders and fully convolutional neural networks, may not be familiar to all students
Overall, this course is a great option for students who want to learn about GANs and develop the skills to use them for their own projects
Save this course
Save Building Generative Adversarial Networks to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Building Generative Adversarial Networks with these
activities:
Revisit PyTorch Basics
Show steps
Refresh your knowledge of PyTorch to ensure you're comfortable with its core concepts.
Browse courses on
PyTorch
Show steps
-
Review online tutorials and documentation
-
Build a simple neural network using PyTorch
-
Explore examples and resources to enhance your understanding
Review Linear Algebra
Show steps
Reinforce your understanding of linear algebra concepts to strengthen your foundation for this course.
Browse courses on
Linear Algebra
Show steps
-
Revisit your class notes and textbooks
-
Solve practice problems to test your comprehension
-
Participate in online forums or study groups to clarify any doubts
Read 'Generative Adversarial Networks' by Goodfellow
Show steps
Deepen your understanding of GANs by reading this seminal book, which provides a comprehensive overview of the field.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
-
Read through the chapters covering the foundational concepts of GANs
-
Work through the exercises and examples to reinforce your learning
-
Summarize the key takeaways from the book in your own words
One other activity
Expand to see all activities and additional details
Show all four activities
Follow Tutorials on Conditional GANs
Show steps
Enhance your GAN knowledge by exploring tutorials on conditional GANs, which generate images based on specific conditions.
Show steps
-
Search for online tutorials or courses on conditional GANs
-
Follow the tutorials step-by-step, implementing the code and understanding the underlying concepts
-
Modify the code to experiment with different conditions and data
Revisit PyTorch Basics
Show steps
Refresh your knowledge of PyTorch to ensure you're comfortable with its core concepts.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Review online tutorials and documentation
- Build a simple neural network using PyTorch
- Explore examples and resources to enhance your understanding
Review Linear Algebra
Show steps
Reinforce your understanding of linear algebra concepts to strengthen your foundation for this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit your class notes and textbooks
- Solve practice problems to test your comprehension
- Participate in online forums or study groups to clarify any doubts
Read 'Generative Adversarial Networks' by Goodfellow
Show steps
Deepen your understanding of GANs by reading this seminal book, which provides a comprehensive overview of the field.
View
Deep Learning (Adaptive Computation and Machine...
on Amazon
Show steps
Show steps
Show steps
- Read through the chapters covering the foundational concepts of GANs
- Work through the exercises and examples to reinforce your learning
- Summarize the key takeaways from the book in your own words
One other activity
Expand to see all activities and additional details
Show all four activities
Follow Tutorials on Conditional GANs
Show steps
Enhance your GAN knowledge by exploring tutorials on conditional GANs, which generate images based on specific conditions.
Show steps
Show steps
Show steps
- Search for online tutorials or courses on conditional GANs
- Follow the tutorials step-by-step, implementing the code and understanding the underlying concepts
- Modify the code to experiment with different conditions and data
Career center
Learners who complete Building Generative Adversarial Networks will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement AI systems. Generative Adversarial Networks (GANs) are a significant advancement in AI, enabling the creation of realistic synthetic data and images. This course provides a comprehensive introduction to GANs, covering their theory, implementation, and applications. By mastering GANs, you'll enhance your ability to develop and deploy cutting-edge AI systems across various industries and domains.
Data Scientist
Data Scientist
Data Scientists build and evaluate machine learning models to gain insights from data. This course introduces Generative Adversarial Networks (GANs), a powerful technique used in image generation and manipulation. GANs help create realistic images, and this course teaches you to build and train them. As a Data Scientist, GANs can enhance your ability to develop innovative solutions in various fields such as healthcare, finance, and manufacturing. By mastering GANs, you'll be better equipped to drive data-driven decision-making and contribute to cutting-edge data science applications.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and applying deep learning models. Generative Adversarial Networks (GANs) are a fundamental concept in deep learning, particularly in image and data generation. This course provides a comprehensive introduction to GANs, covering their architecture, training techniques, and applications. Mastering GANs through this course will enhance your skills as a Deep Learning Engineer and empower you to create cutting-edge deep learning solutions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers deploy and maintain machine learning models. This course focuses on Generative Adversarial Networks (GANs), a technique for generating realistic data. Understanding GANs can empower you to build and optimize machine learning models that excel in tasks like image generation, data augmentation, and text-to-image synthesis. This course provides a solid foundation in GANs, enhancing your ability to contribute to the development and deployment of advanced machine learning systems.
Research Scientist
Research Scientist
Research Scientists conduct research and development in various scientific fields. Generative Adversarial Networks (GANs) have emerged as a powerful tool for data generation and manipulation, making them highly relevant to Research Scientists. This course offers a solid foundation in GANs, covering their architecture, training techniques, and applications. By completing this course, you'll gain the knowledge and skills necessary to leverage GANs in your research projects, advancing your contributions to the scientific community.
Data Science Manager
Data Science Manager
Data Science Managers lead and manage teams of data scientists and analysts. Generative Adversarial Networks (GANs) are gaining prominence in data science, offering powerful techniques for data generation and manipulation. This course provides a comprehensive overview of GANs, covering their architecture, training techniques, and applications. By understanding GANs, you'll be better equipped to guide your team in leveraging this technology, leading to more innovative and effective data science solutions.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop systems for computers to interpret and understand visual data. Generative Adversarial Networks (GANs) are essential in computer vision, particularly in image generation and manipulation. This course teaches you the theory and implementation of GANs, providing you with the skills to build and train your own GAN models. By completing this course, you'll gain a competitive edge in the field of computer vision and be better equipped to develop innovative vision-based applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Knowledge of Generative Adversarial Networks (GANs) can benefit Software Engineers working on computer vision, natural language processing, and image generation projects. This course provides a practical introduction to GANs, teaching you to build and train your own GAN models. By mastering GANs, you'll expand your software development toolkit and enhance your ability to deliver innovative and cutting-edge software solutions.
Game Developer
Game Developer
Game Developers design and develop video games. Generative Adversarial Networks (GANs) are gaining traction in game development, particularly in content generation and procedural level design. This course provides a practical introduction to GANs, teaching you how to generate realistic images and data. By understanding GANs, you'll be better equipped to create visually stunning and immersive game environments that captivate your players.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing strategies to promote products and services. Generative Adversarial Networks (GANs) are emerging as a valuable tool for marketers, particularly in content creation and personalization. This course offers a practical introduction to GANs, teaching you how to generate realistic images and data. By understanding GANs, you'll be better equipped to create engaging and effective marketing campaigns that resonate with your target audience.
Data Analyst
Data Analyst
Data Analysts interpret and communicate data to inform decision-making. Knowledge of Generative Adversarial Networks (GANs) can be valuable for Data Analysts looking to explore advanced data analysis techniques. This course introduces GANs and their applications in data generation, image manipulation, and statistical modeling. By understanding GANs, you can expand your toolkit for data exploration and analysis, leading to more informed and data-driven decision-making.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and apply mathematical and statistical models to financial data. Generative Adversarial Networks (GANs) are gaining traction in quantitative finance, particularly in data generation and risk assessment. This course offers a comprehensive introduction to GANs, covering their theory, implementation, and applications in finance. By understanding GANs, you'll enhance your ability to develop and deploy sophisticated financial models, leading to better decision-making and risk management.
Product Manager
Product Manager
Product Managers lead the development and launch of new products. Knowledge of Generative Adversarial Networks (GANs) can be beneficial for Product Managers working on products involving image generation, data augmentation, and personalization. This course provides a practical introduction to GANs, teaching you the basics of their architecture and applications. By understanding GANs, you'll be better equipped to evaluate and integrate this technology into your product development process, enhancing the value and innovation of your products.
User Experience Designer
User Experience Designer
User Experience Designers enhance the usability and satisfaction of digital products and services. Generative Adversarial Networks (GANs) have the potential to revolutionize user experience design, particularly in personalization and content generation. This course provides a foundational understanding of GANs, covering their theory, applications, and limitations. By mastering GANs, you'll be better equipped to create intuitive and engaging user experiences that meet the evolving needs of your users.
Business Analyst
Business Analyst
Business Analysts identify and analyze business needs and develop solutions to improve efficiency. Generative Adversarial Networks (GANs) have the potential to revolutionize data analysis and decision-making in various business contexts. This course provides a foundational understanding of GANs, covering their theory, applications, and limitations. By mastering GANs, you'll enhance your ability to analyze data, identify patterns, and develop innovative solutions that drive business growth and success.
For more career information including salaries, visit:
OpenCourser.com/course/wdp89a/building
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
Building Generative Adversarial Networks.
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 models, and applications. It valuable resource for anyone interested in learning more about deep learning.
Provides a comprehensive overview of the mathematical foundations of machine learning, covering the different concepts, techniques, and applications. It valuable resource for anyone interested in learning more about the mathematical foundations of machine learning.
Provides a comprehensive overview of probabilistic graphical models, covering the different types, algorithms, and applications. It valuable resource for anyone interested in learning more about probabilistic graphical models.
Save
Provides a comprehensive overview of computer vision, covering the different algorithms, techniques, and applications. It valuable resource for anyone interested in learning more about computer vision.
Provides a comprehensive overview of natural language processing with deep learning, covering the different algorithms, techniques, and applications. It valuable resource for anyone interested in learning more about natural language processing with deep learning.
Provides a comprehensive overview of reinforcement learning, covering the different algorithms, techniques, and applications. It valuable resource for anyone interested in learning more about reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wdp89a/building
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Effort
4 weeks
Level
Intermediate
Via
Udacity
Instructors
Giacomo Vianello
Nathan Klarer
Erick Galinkin
Thomas Hossler
Language
English
Good to know
Explores GANs, which are increasingly used to generate images, videos, and audio data for a variety of purposes, such as creating realistic-looking avatars for virtual reality games or generating synthetic data for training self-driving cars
Taught by Giacomo Vianello, Nathan Klarer, Erick Galinkin, and Thomas Hossler, who are recognized experts in the field of deep learning and artificial intelligence
Covers the basics of GANs and then dives into more advanced topics, such as conditional GANs, GANs for image-to-image translation, and GANs for video generation
Requires students to have a strong foundation in deep learning, as well as experience with Python and PyTorch
Some of the prerequisite knowledge, such as variational autoencoders and fully convolutional neural networks, may not be familiar to all students
Overall, this course is a great option for students who want to learn about GANs and develop the skills to use them for their own projects
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