Hands-On Generative Adversarial Networks with PyTorch 1.x
This book will help you understand how GANs architecture works using PyTorch. You will get familiar with the most flexible deep learning toolkit and use it to transform ideas into actual working codes. You will apply GAN models to areas like computer vision, multimedia and natural language processing using a sample-generation perspective.
Offered by
Difficulty Level
Intermediate
Completion Time
10h24m
Language
English
About Book
Who Is This Book For?
This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You’ll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.
Hands-On Generative Adversarial Networks with PyTorch 1.x
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 10h24m total length
Generative Adversarial Networks Fundamentals
Getting Started with PyTorch 1.3
Best Practices for Model Design and Training
Building Your First GAN with PyTorch
Generating Images Based on Label Information
Image-to-Image Translation and Its Applications
Image Restoration with GANs
Training Your GANs to Break Different Models
Image Generation from Description Text
Sequence Synthesis with GANs
Reconstructing 3D models with GANs
Related Resources
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