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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.

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Overview

Content

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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.

Generative adversarial networks (GANs) are machine learning models that generate data resembling a given dataset. GANs have two ...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

Knowledge of GAN fundamentals and PyTorch features

Content

55 Lessons10 Quizzes

Getting Started

1 Lessons

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

Introduction to the Course

Generative Adversarial Networks Fundamentals

5 Lessons

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

5 Lessons

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

Building Our First GAN with PyTorch

5 Lessons

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

Generating Images Based on Label Information

5 Lessons

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

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 Lessons

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.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Packt

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