Build Better 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.
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پښتو, বাংলা, اردو, 2 more
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What you'll learn
Implement and evaluate GANs using Fréchet Inception Distance (FID)
Master StyleGAN architecture and implementation techniques
Identify and address bias in generative models
Compare different GAN evaluation metrics and their applications
Understand the advantages and limitations of various generative models
Skills you'll gain
This course includes:
2.3 Hours PreRecorded video
1 assignment
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There are 3 modules in this course
This comprehensive course focuses on advanced techniques in Generative Adversarial Networks (GANs). Students learn to evaluate GAN performance using methods like Fréchet Inception Distance (FID), understand and address bias in generative models, and implement state-of-the-art StyleGAN architecture. The curriculum covers both theoretical foundations and practical applications, including GAN alternatives and limitations. Special emphasis is placed on ethical considerations and bias detection in machine learning models.
Week 1: Evaluation of GANs
Module 1 · 9 Hours to complete
Week 2: GAN Disadvantages and Bias
Module 2 · 11 Hours to complete
Week 3: StyleGAN and Advancements
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.
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