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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.
55 Lessons
16h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
2.
Generative Adversarial Networks Fundamentals
Generative Adversarial Networks Fundamentals
Discover the logic behind GANs, their adversarial process, functions, and diverse applications.
3.
Best Practices for Model Design and Training
Best Practices for Model Design and Training
5 Lessons
5 Lessons
Go hands-on with designing and training GANs, optimizing parameters, and efficient coding in Python.
4.
Building Our First GAN with PyTorch
Building Our First GAN with PyTorch
5 Lessons
5 Lessons
Apply your skills to build, train, and explore DCGANs with PyTorch for image generation.
5.
Generating Images Based on Label Information
Generating Images Based on Label Information
5 Lessons
5 Lessons
Solve problems in generating labeled images using CGANs, Fashion-MNIST, and InfoGAN.
6.
Image-to-Image Translation and Its Applications
Image-to-Image Translation and Its Applications
5 Lessons
5 Lessons
Tackle image-to-image translation models, including pix2pix, pix2pixHD, and CycleGAN applications.
7.
Image Restoration with GANs
Image Restoration with GANs
6 Lessons
6 Lessons
Practice using GANs for super-resolution, inpainting, and enhancing image quality with SRGAN and WGAN.
8.
Training GANs to Break Different Models
Training GANs to Break Different Models
3 Lessons
3 Lessons
Step through creating adversarial examples using GANs to challenge deep learning models.
9.
Image Generation from Description Text
Image Generation from Description Text
5 Lessons
5 Lessons
Get started with text-to-image synthesis using GANs, advanced architectures, and StackGAN++.
10.
Sequence Synthesis with GANs
Sequence Synthesis with GANs
4 Lessons
4 Lessons
Go hands-on with SeqGAN for text generation and SEGAN for speech enhancement.
11.
Reconstructing 3D Models with GANs
Reconstructing 3D Models with GANs
3 Lessons
3 Lessons
Enhance your skills in 3D object representation, GANs design, and training techniques.
13.
Appendix
Appendix
7 Lessons
7 Lessons
Tackle installing PyTorch, setting up GPU acceleration, and exploring the C++ frontend for efficient training.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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 image generation with label info, image-to-image translation with pix2pix and CycleGAN, and image restoration techniques. The course concludes with text-to-image synthesis, sequence synthesis, and 3D model reconstruction, providing a comprehensive understanding of GANs.
This course equips developers with advanced GAN and DL skills. Mastering GANs using PyTorch will enable you to tackle real-world challenges in various domains like image processing and multimedia content generation.
ABOUT THE AUTHOR
Packt
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Software Developer
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Front-end Developer
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Vinay Krishnaiah
Software Developer
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