Overview
Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs.
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.
About this Specialization
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 Generative Adversarial Networks (GANs) Specialization from Coursera in partnership with Deeplearning 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.
Applied Learning Project
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.
Get more details
Visit programme websiteProgramme Structure
Courses include:
Build Basic Generative Adversarial Networks (GANs)
Build Better Generative Adversarial Networks (GANs)
Apply Generative Adversarial Networks (GANs)
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 2 months
- 10 hrs/week
Start dates & application deadlines
Language
Delivered
- Self-paced
Disciplines
Computer Sciences Web Technologies & Cloud Computing Machine Learning View 523 other Short Courses in Web Technologies & Cloud Computing in United StatesExplore more key information
Visit programme websiteAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Intermediate Level
- Basic calculus, linear algebra, stats
- Grasp of AI, deep learning & CNNs
- Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
Make sure you meet all requirements
Visit programme websiteTuition Fee
-
International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 months. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 months.
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Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.