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Build Basic Generative Adversarial Networks (GANs)

Approx. 31 hours to complete

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Course Summary

Learn to build basic generative adversarial networks (GANs) in this course. Gain hands-on experience in working with GANs and use them to generate images and music.

Key Learning Points

Learn the basics of GANs and how they work
Build your own GANs using TensorFlow
Create and generate images and music using GANs

Learning Outcomes

Understand the fundamentals of GANs
Build your own GANs using TensorFlow
Create and generate images and music using GANs

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Generative Adversarial Networks (GANs) Specialization
Deep Learning Specialization

Related Education Paths

Notable People in This Field

Ian Goodfellow
Yann LeCun

Related Books

Description

In this course, you will:

- Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories 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 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.

Outline

Week 1: Intro to GANs
Welcome to the Specialization
Welcome to Week 1
Generative Models
Real Life GANs
Intuition Behind GANs
Discriminator
Generator
BCE Cost Function
Putting It All Together
(Optional) Intro to PyTorch
Syllabus
Connect with your mentors and fellow learners on Slack!
Check out some non-existent people!
Pre-trained Model Exploration
Inputs to a Pre-trained GAN
Works Cited
How to Refresh your Workspace

Week 2: Deep Convolutional GANs
Welcome to Week 2
Activations (Basic Properties)
Common Activation Functions
Batch Normalization (Explained)
Batch Normalization (Procedure)
Review of Convolutions
Padding and Stride
Pooling and Upsampling
Transposed Convolutions
(Optional) A Closer Look at Transposed Convolutions
(Optional) The DCGAN Paper
(Optional Notebook) GANs for Video
Works Cited

Week 3: Wasserstein GANs with Gradient Penalty
Welcome to Week 3
Mode Collapse
Problem with BCE Loss
Earth Mover’s Distance
Wasserstein Loss
Condition on Wasserstein Critic
1-Lipschitz Continuity Enforcement
(Optional Notebook) ProteinGAN
(Optional) The WGAN and WGAN-GP Papers
(Optional) WGAN Walkthrough
Works Cited

Week 4: Conditional GAN & Controllable Generation
Welcome to Week 4
Conditional Generation: Intuition
Conditional Generation: Inputs
Controllable Generation
Vector Algebra in the Z-Space
Challenges with Controllable Generation
Classifier Gradients
Disentanglement
Conclusion of Course 1
(Optional) The Conditional GAN Paper
(Optional) An Example of a Controllable GAN
Works Cited
Acknowledgments

Summary of User Reviews

Learn how to build basic Generative Adversarial Networks (GANs) with this course on Coursera. Students have praised the course for its clear explanations and hands-on approach. Overall, the course has received positive feedback from users.

Key Aspect Users Liked About This Course

Hands-on approach to learning

Pros from User Reviews

Clear explanations of complex concepts
Great practical exercises
Engaging instructor
Good pacing of lessons
Useful resources provided

Cons from User Reviews

Some concepts may be too advanced for beginners
Lack of depth in some areas
Not enough examples provided
Limited interaction with the instructor
Some technical issues with the platform

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