Apply Generative Adversarial Networks (GANs)
This course is part of Generative Adversarial Networks (GANs).
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Generative Adversarial Networks (GANs) Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.8
(518 ratings)
25,247 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
Powered by
What you'll learn
Implement Pix2Pix for paired image-to-image translation
Develop CycleGAN for unpaired image translation
Apply GANs for data augmentation and privacy preservation
Create U-Net architectures for image generation
Understand GAN applications in privacy and anonymity
Master advanced GAN architectures and loss functions
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 3 modules in this course
This advanced course explores practical applications of Generative Adversarial Networks (GANs) in image-to-image translation and data augmentation. Students learn to implement sophisticated GAN architectures including Pix2Pix for paired image translation and CycleGAN for unpaired translation. The curriculum covers privacy preservation, data anonymization, and ethical considerations in GAN applications, combining theoretical understanding with hands-on implementation using PyTorch.
GANs for Data Augmentation and Privacy
Module 1 · 7 Hours to complete
Image-to-Image Translation with Pix2Pix
Module 2 · 10 Hours to complete
Unpaired Translation with CycleGAN
Module 3 · 7 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Generative Adversarial Networks (GANs)
Instructors
Instructor
haron Zhou is a highly accomplished PhD candidate in Computer Science at Stanford University, where she is advised by the renowned AI expert Andrew Ng. Her academic work and research span the theoretical and applied aspects of Artificial Intelligence (AI), with a focus on Generative Adversarial Networks (GANs), machine learning, and applications that drive social good, including medicine and climate science. Sharon has a passion for bridging the gap between cutting-edge AI technology and its real-world applications for human welfare.Before embarking on her PhD journey, Sharon built a strong foundation in machine learning product management, having worked at Google and various startups. Her experience at Google honed her ability to apply machine learning and AI to create impactful products and solutions for large-scale audiences. Sharon's diverse academic background includes a degree in Computer Science and Classics from Harvard University, giving her a unique interdisciplinary perspective on AI and technology.Sharon’s approach to AI is deeply human-centered, with a clear passion for using technology to address pressing global challenges. While she is enthusiastic about the power and potential of AI, particularly Generative Adversarial Networks (GANs), she values the importance of human understanding and context in the development and deployment of AI systems.
Curriculum Developer and Cybersecurity Enthusiast at DeepLearning.AI
Eda Zhou is a Curriculum Developer at DeepLearning.AI, where she brings her expertise in AI, machine learning, and cybersecurity to create educational content that empowers learners in these fast-evolving fields. Eda holds both a Bachelor's and Master's degree in Computer Science from Worcester Polytechnic Institute (WPI), with a specialized focus on cybersecurity. Her strong academic background and hands-on experience drive her passion for applying new technologies, particularly in the realms of AI and machine learning, to safeguard computer networks and protect users.Eda’s interests extend into exploring how cutting-edge machine learning techniques, such as Generative Adversarial Networks (GANs), can be applied to enhance security measures. Her curiosity about the intersection of AI and cybersecurity has led her to focus on building innovative solutions for threat detection, data security, and overall protection of digital infrastructures.As a curriculum developer at DeepLearning.AI, Eda has contributed to the development of several Generative Adversarial Networks (GANs) courses, helping learners grasp the fundamental and advanced aspects of this powerful AI technique. Her courses guide students through building, applying, and enhancing GANs, providing them with the knowledge to leverage these models in various applications, including data generation and security.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.