Deep Learning for Computer Vision with PyTorch: Create Powerful AI Solutions, Accelerate Production, and Stay Ahead with Transformers and Diffusion Mo

Younker, William L.

ISBN 13: 9798264710391

Published by Independently published, 2025

About this Item

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Bibliographic Details

• Title: Deep Learning for Computer Vision with PyTorch: Create Powerful AI Solutions, Accelerate Production, and Stay Ahead with Transformers and Diffusion Mo
• Publisher: Independently published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798264710391
• Binding: Soft cover
• Condition: As New

About this title

Synopsis

Deep Learning for Computer Vision with PyTorch: Create Powerful AI Solutions, Accelerate Production, and Stay Ahead with Transformers and Diffusion Models

Can you turn raw images into business insights, creative breakthroughs, or the next industry-standard product?
Today’s computer vision is more than algorithms—it’s the foundation for innovation in every field, from healthcare and finance to entertainment and AI-powered apps. Yet, with so many rapid advances, even experienced developers face a crucial question: How do you build, scale, and ship state-of-the-art vision solutions that actually work in the real world?

Deep Learning for Computer Vision with PyTorch puts the latest breakthroughs—Vision Transformers, diffusion models, transfer learning, and production-ready pipelines—directly into your hands. This book is designed for engineers, data scientists, and forward-thinking developers who want practical, code-driven answers and results you can trust.

Master every essential skill, including:

• Constructing high-performance vision pipelines with PyTorch 2.x, TorchVision, and powerful augmentation tools

• Harnessing CNNs, Vision Transformers (ViT), and generative diffusion models for image classification, detection, segmentation, and creative tasks

• Leveraging transfer learning for rapid prototyping and instant accuracy boosts

• Optimizing and deploying robust models with TorchScript, TorchServe, and Docker

• Integrating monitoring, drift detection, and reproducibility practices that set your production systems apart

• Navigating real-world challenges—data imbalance, bias, privacy, adversarial testing, and ethical deployment

You’ll build hands-on projects from scratch to scalable API services, with every chapter anchored in code you can run, extend, and use today. This is your guide to staying ahead—whether you’re automating workflows, shipping cloud apps, or inventing new creative tools.