Hands-On Generative Adversarial Networks with PyTorch

In this GAN course, learn GAN fundamentals and PyTorch. Explore DCGANs, conditional GANs, image translations, and text-to-image synthesis to master advanced skills for real-world applications.

Advanced

55 Lessons

16h

Certificate of Completion

In this GAN course, learn GAN fundamentals and PyTorch. Explore DCGANs, conditional GANs, image translations, and text-to-image synthesis to master advanced skills for real-world applications.

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This course includes

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10 Quizzes

This course includes

8 AI Feedbacks

29 Playgrounds

10 Quizzes

Course Overview

Generative adversarial networks (GANs) are machine learning models that generate data resembling a given dataset. GANs have two neural networks: the generator and the discriminator. PyTorch is a popular deep learning framework that is efficient for GAN implementation due to its dynamic computation capabilities. The course begins with what are GANs, activation functions, and model training best practices. You’ll build your first GAN with PyTorch, exploring DCGANs and conditional GANs. Then, you’ll learn ima...Show More

What You'll Learn

Knowledge of GAN fundamentals and PyTorch features

Hands-on experience building GANs with PyTorch

Proficiency in model design and training

An understanding of adversarial learning and breaking different models

Application of GANs in diverse domains like computer vision and NLP

Familiarity with training challenges, required resources, and their results

What You'll Learn

Knowledge of GAN fundamentals and PyTorch features

Course Content

Getting Started

Get familiar with GANs, their architecture, and hands-on applications using PyTorch.

Introduction to the Course

Generative Adversarial Networks Fundamentals

Discover the logic behind GANs, their adversarial process, functions, and diverse applications.

Fundamentals of Machine Learning Introducing Adversarial Learning Defining Activation Functions and The Loss Function What Can GANs Do?Quiz: Generative Adversarial Network Fundamentals

Best Practices for Model Design and Training

Go hands-on with designing and training GANs, optimizing parameters, and efficient coding in Python.

Model Design Cheat Sheet Parameter and Loss Function Optimizations and Learning Rate Efficient Coding in Python Quiz: Best Practices for Model Design and Training

Building Our First GAN with PyTorch

Apply your skills to build, train, and explore DCGANs with PyTorch for image generation.

Introduction to Deep Convolutional GANs Creating a DCGAN with PyTorch Moving to Larger Datasets Having Fun with the Generator Network Quiz: Building Our First GAN with PyTorch

Generating Images Based on Label Information

Solve problems in generating labeled images using CGANs, Fashion-MNIST, and InfoGAN.

CGANs: How Are Labels Used?Generating Images from Labels with the CGAN Working with the Fashion-MNIST Dataset InfoGAN—Unsupervised Attribute Extraction Quiz: Generating Images Based on Label Information

Image-to-Image Translation and Its Applications

5 Lessons

Tackle image-to-image translation models, including pix2pix, pix2pixHD, and CycleGAN applications.

Image Restoration with GANs

6 Lessons

Practice using GANs for super-resolution, inpainting, and enhancing image quality with SRGAN and WGAN.

Training GANs to Break Different Models

3 Lessons

Step through creating adversarial examples using GANs to challenge deep learning models.

Image Generation from Description Text

5 Lessons

Get started with text-to-image synthesis using GANs, advanced architectures, and StackGAN++.

10.

Sequence Synthesis with GANs

4 Lessons

Go hands-on with SeqGAN for text generation and SEGAN for speech enhancement.

11.

Reconstructing 3D Models with GANs

3 Lessons

Enhance your skills in 3D object representation, GANs design, and training techniques.

12.

Concluding Remarks

1 Lesson

Equip yourself with essential GAN knowledge and hands-on PyTorch skills for real-world applications.

13.

Appendix

7 Lessons

Tackle installing PyTorch, setting up GPU acceleration, and exploring the C++ frontend for efficient training.

Course Author

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