Generative Adversarial Networks (GANs) Specialization

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  Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

• Is financial aid available?

  Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

• Can I take the course for free?

  When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

• Is this course really 100% online? Do I need to attend any classes in person?

  This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

• Will I earn university credit for completing the Specialization?

  This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

• What are GANs?

  Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. They are algorithmic architectures that use two neural networks, pitting one against the other in order to generate new instances of data.

• What are the applications of GANs?

  Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more.  

• Why are GANs important?

  As computing power has increased, so has the popularity of GANs and its capabilities. GANs have opened up many new directions: from generating high amounts of datasets for training machine learning models and allowing for powerful unsupervised learning models to producing sharper, discrete, and more accurate outputs. GANs have also informed research in adjacent areas like adversarial learning, adversarial examples and attacks, model robustness, etc.

• What is the GANs Specialization about?

  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. By the end, you would have trained your own model using PyTorch, used it to create images, and evaluated a variety of advanced GANs.

• What will I learn in the GANs Specialization?

  Specialization: Gain practical knowledge of how generative models work. Construct and design your own generative adversarial model. Analyze how generative models are being applied in various commercial and exploratory applications.

  Course 1: In this course, you will understand the fundamental components of GANs, build a basic GAN using PyTorch, use convolutional layers to build advanced DCGANs that processes images, apply W-Loss function to solve the vanishing gradient problem, and learn how to effectively control your GANs and build conditional GANs.

  Course 2: In this course, you will understand the challenges of evaluating GANs, compare different generative models, use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs, identify sources of bias and the ways to detect it in GANs, and learn and implement the techniques associated with the state-of-the-art StyleGAN.

  Course 3: In this course, you will use GANs for data augmentation and privacy preservation, survey more applications of GANs, and build Pix2Pix and CycleGAN for image translation.

• Who is the GANs Specialization for?

  This Specialization is for software engineers, students, and researchers from any field, who are interested in machine learning and want to understand how GANs work.

  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.

• What background knowledge is necessary?

  Learners should have a working knowledge of AI, deep learning, and convolutional neural networks. They should have intermediate Python skills as well as some experience with any deep learning framework (TensorFlow, Keras, or PyTorch). Learners should be proficient in basic calculus, linear algebra, and statistics.

  We highly recommend that you complete the Deep Learning Specialization prior to starting the GANs Specialization.

• What will I be able to do upon completing the Specialization?

  After completing this Specialization, you will have learned how to achieve the state-of-the-art in realistic generation. You will be able to generate realistic images, edit those images by controlling the output in a number of ways (eg. convert a horse to a zebra or lengthen your hair or make yourself older), quantitatively compare generators, convert an image to another (eg. turning a sketch into a photo-realistic version), animate still images, solve many of the challenges that GANs are notorious for, and more.

• Who created the GANs Specialization?

  This Specialization was created by Sharon Zhou, a CS PhD candidate at Stanford University, advised by Andrew Ng. Sharon Zhou’s work in AI spans from theoretical to applied, in medicine, climate, and more broadly, social good. Previously a machine learning product manager at Google and various startups, Sharon is a Harvard graduate in CS and Classics. She likes humans more than AI, though GANs occupy a special place in her heart.

• Is this a standalone course or a Specialization?

  This is a Specialization made up of 3 courses.

• Do I need to take the courses in a specific order?

  We recommend taking the courses in the prescribed order for a logical and thorough learning experience.

• Can I audit the Specialization?

  You can audit the courses in the Specialization for free. You will not receive a certificate at the end if you choose to audit it for free instead of purchasing it.

• How long does it take to complete the Specialization?

  This specialization consists of three courses. At the rate of 5 hours a week, it typically takes 4  weeks to complete each course.

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