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Hands-On Generative Adversarial Networks with PyTorch

Gain insights into GAN fundamentals and PyTorch. Delve into DCGANs, conditional GANs, image translations, and text-to-image synthesis to master advanced GAN skills for real-world applications.

Advanced

55 Lessons

16h

Certificate of Completion

Gain insights into GAN fundamentals and PyTorch. Delve into DCGANs, conditional GANs, image translations, and text-to-image synthesis to master advanced GAN skills for real-world applications.

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Course Overview

What You'll Learn

Course Content

Course Overview

Generative Adversarial Networks (GANs) are a class of machine learning models used to generate data resembling a given dataset. In a GAN, two neural networks, the generator and discriminator, compete. PyTorch is a popular deep learning (DL) framework that is efficient for GAN implementation due to its dynamic computation capabilities. The course begins with GAN basics, activation functions, and model training best practices. You’ll build your first GAN with PyTorch, exploring DCGANs and conditional GANs. T...Show More

Generative Adversarial Networks (GANs) are a class of machine learning models used to generate data resembling a given dataset. ...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

Course Content

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.

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

5 Lessons

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

5 Lessons

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.

Using Pixel-Wise Labels to Translate Images with pix2pix

Training and Evaluation of pix2pix

Pix2pixHD: High-Resolution Image Translation

CycleGAN: Image-to-Image Translation from Unpaired Collections

Quiz: Image-to-image Translation from Unpaired Collections

Image Restoration with GANs

6 Lessons

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

Image Super-Resolution with SRGAN

Training SRGAN to Generate High-Resolution Images

Generative Image Inpainting

WGAN—Understanding the Wasserstein Distance

Training GAN for image inpainting

Quiz: Image Restoration with GANs

Training GANs to Break Different Models

3 Lessons

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

Adversarial Examples: Attacking Deep Learning Models

Generative Adversarial Examples: Working with Classifier

Quiz: Training GANs to Break Different Models

Image Generation from Description Text

5 Lessons

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

Text-to-Image Synthesis with GANs

GAN Architecture and Training

Generating Photo-Realistic Images with StackGAN++

Training StackGAN++ to Generate Images with Better Quality

Quiz: Image Generation from Description Text

10.

Sequence Synthesis with GANs

4 Lessons

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

Text Generation via SeqGAN

Creating Our Vocabulary for Training

Speech Quality Enhancement with SEGAN

Quiz: Sequence Synthesis with GANs

11.

Reconstructing 3D Models with GANs

3 Lessons

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

Fundamental Concepts in Computer Graphics

Designing GANs for 3D Data Synthesis

Quiz: Reconstructing 3D Models with GANs

12.

Concluding Remarks

1 Lessons

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

Conclusion

13.

Appendix

7 Lessons

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

Introduction to PyTorch

Required Downloads

The C++ Frontend

CUDA—GPU Acceleration for Fast Training and Evaluation

Installing PyTorch on Windows and Linux

Checking GPU Usage Information

Steps to Reach the C++ Source Code for nn.PixelShuffle

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