Generative Adversarial Networks (GANs), Short Course | Part time online | Coursera | United States
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About

This Generative Adversarial Networks (GANs) Specialization from Coursera in partnership with Deeplearning is for software engineers, students, and researchers from any field, who are interested in machine learning and want to understand how GANs work. 

Visit the Visit programme website for more information

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.

Programme Structure

Courses include:

  • Build Basic Generative Adversarial Networks (GANs)

  • Build Better Generative Adversarial Networks (GANs)

  • Apply Generative Adversarial Networks (GANs)

Key information

Duration

  • Part-time
    • 2 months
    • 10 hrs/week

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online
  • Self-paced

Academic 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)

Tuition Fee

To always see correct tuition fees
  • International

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 2 months.
  • National

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 2 months.

You can choose from hundreds of free courses, or get a degree or certificate at a breakthrough price. You can now select Coursera Plus, an annual subscription that provides unlimited access.

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.

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