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URL: https://www.coursera.org/learn/introduction-course-to-autoencoders-vaes-and-gans

⇱ Introduction Course to Autoencoders, VAEs, and GANs | Coursera

Introduction Course to Autoencoders, VAEs, and GANs

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Introduction Course to Autoencoders, VAEs, and GANs

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build and train Autoencoders and VAEs using TensorFlow

  • Use VAEs for generating synthetic data like images

  • Understand and apply GAN architecture and training techniques

  • Create realistic outputs with GANs for real-world use cases

Details to know

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Assessments

7 assignments

Taught in English

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This course is part of the Generative AI Models and Transformer Networks Certification Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 2 modules in this course

This deep learning course provides a comprehensive introduction to Autoencoders, Variational Autoencoders (VAE), and Generative Adversarial Networks (GANs). Begin by exploring how autoencoders compress and reconstruct data, and discover how VAEs add probabilistic modeling to enhance generative capabilities. Learn the VAE training process and implement a VAE using TensorFlow for image generation with the MNIST dataset. Progress to mastering GANs—understand their adversarial training approach, how the generator and discriminator interact, and explore real-world applications. Gain hands-on experience by building a GAN to generate realistic fake images through step-by-step demos.

To be successful in this course, you should have a basic understanding of neural networks, machine learning concepts, and Python programming. By the end of this course, you’ll be able to: - Implement and train autoencoders and VAEs - Apply VAEs for generative tasks like image synthesis - Build and train GANs to generate realistic data - Understand and apply adversarial training in real-world use cases Ideal for aspiring AI developers, ML engineers, and data scientists exploring generative deep learning.

Explore the fundamentals of Autoencoders and Variational Autoencoders (VAE) in this module. Learn how autoencoders compress and reconstruct data, the challenges they face, and how VAEs overcome them. Understand the VAE training process and its generative capabilities. Gain hands-on experience by implementing a VAE with TensorFlow for image generation using the MNIST dataset.

What's included

8 videos1 reading4 assignments

8 videos•Total 46 minutes
  • Learning Objectives•1 minute
  • Autoencoders•6 minutes
  • Challenges in Autoencoders•3 minutes
  • Introdution to Variational Autoencoders•6 minutes
  • VAE Generative Training Process•5 minutes
  • Steps Involved in VAE•4 minutes
  • Image Generation•5 minutes
  • Demo: Implementing a VAE with TensorFlow for Image Generation Using the MNIST Dataset•16 minutes
1 reading•Total 10 minutes
  • Course Syllabus •10 minutes
4 assignments•Total 85 minutes
  • Quiz on Introduction to Autoencoders•15 minutes
  • Quiz on VAE Training Process•15 minutes
  • Quiz on VAE Generative Applications•15 minutes
  • Assessment for Autoencoders and Variational Autoencoders (VAE)•40 minutes

Master Generative Adversarial Networks (GANs) in this hands-on module. Learn how GANs work through their unique adversarial training process and explore real-world use cases across industries. Understand generator-discriminator dynamics and how they produce realistic data. Gain practical skills by implementing a GAN to generate fake images with guided demos and code examples.

What's included

4 videos3 assignments

4 videos•Total 25 minutes
  • Introduction to GANs•5 minutes
  • Training Process and Industrial Use Case of GAN•6 minutes
  • Demo: Generating Fake Images with Generative Adversial Networks (GANs)•13 minutes
  • Key Takeaways•1 minute
3 assignments•Total 70 minutes
  • Quiz on Introduction to GANs and Training Process•15 minutes
  • Quiz on Practical Implementation of GANs•15 minutes
  • Assessment for Generative Adversarial Networks (GAN)•40 minutes

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Instructor

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87 Courses•77,755 learners

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Frequently asked questions

GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) are generative models used to create new data samples. GANs use a generator-discriminator setup, while VAEs rely on probabilistic encoding and decoding.

GANs generate realistic data by pitting two networks against each other, while autoencoders compress and reconstruct data. Both are used in unsupervised learning but serve different purposes in data generation and feature learning.

Autoencoders are neural networks designed to learn efficient data representations by encoding input into a compressed form and then decoding it back to reconstruct the original input.

Standard autoencoders compress data deterministically, while VAEs introduce randomness through probabilistic encoding, allowing them to generate new data samples similar to the original.

Common types include vanilla autoencoders, sparse autoencoders, denoising autoencoders, variational autoencoders (VAEs), and convolutional autoencoders—each suited for specific learning tasks.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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.

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