Mastering Computer Vision with PyTorch 2.0

Key Features
? Covers core to advanced Computer Vision topics with PyTorch 2.0’s latest features and best practices.
? Progressive learning path to ensure suitability for beginners and experts alike.
? Tackles practical tasks like optimization, transfer learning, and edge deployment.

Book Description
In an era where Computer Vision has rapidly transformed industries like healthcare and autonomous systems, PyTorch 2.0 has become the leading framework for high-performance AI solutions.[Mastering Computer Vision with PyTorch 2.0]bridges the gap between theory and application, guiding readers through PyTorch essentials while equipping them to solve real-world challenges.

Starting with PyTorch’s evolution and unique features, the book introduces foundational concepts like tensors, computational graphs, and neural networks. It progresses to advanced topics such as Convolutional Neural Networks (CNNs), transfer learning, and data augmentation. Hands-on chapters focus on building models, optimizing performance, and visualizing architectures. Specialized areas include efficient training with PyTorch Lightning, deploying models on edge devices, and making models production-ready.

Explore cutting-edge applications, from object detection models like YOLO and Faster R-CNN to image classification architectures like ResNet and Inception. By the end, readers will be confident in implementing scalable AI solutions, staying ahead in this rapidly evolving field. Whether you’re a student, AI enthusiast, or professional, this book empowers you to harness the power of PyTorch 2.0 for Computer Vision.

What you will learn
? Build and train neural networks using PyTorch 2.0.
? Implement advanced image classification and object detection models.
? Optimize models through augmentation, transfer learning, and fine-tuning.
? Deploy scalable AI solutions in production and on edge devices.
? Master PyTorch Lightning for efficient training workflows.
? Apply real-world techniques for preprocessing, quantization, and deployment.

Table of Contents
1. Diving into PyTorch 2.0
2. PyTorch Basics
3. Transitioning from PyTorch 1.x to PyTorch 2.0
4. Venturing into Artificial Neural Networks
5. Diving Deep into Convolutional Neural Networks (CNNs)
6. Data Augmentation and Preprocessing for Vision Tasks
7. Exploring Transfer Learning with PyTorch
8. Advanced Image Classification Models
9. Object Detection Models
10. Tips and Tricks to Improve Model Performance
11. Efficient Training with PyTorch Lightning
12. Model Deployment and Production-Ready Considerations
Index

About the Authors
M. Arshad Siddiquiis a distinguished computer vision expert with extensive experience in developing and deploying cutting-edge AI solutions. His career began as a Computer Vision Engineer at Lensbricks, where he developed innovative vision systems for emerging technologies. He then advanced to Big Vision, refining his expertise in tackling large-scale challenges in computer vision and artificial intelligence.

Currently a Principal Engineer in Computer Vision and AI, Arshad has collaborated with over 20 organizations, ranging from dynamic startups to Fortune 500 companies, helping them design and implement robust AI solutions. Over the course of his career, he has worked across diverse industries, including healthcare, retail, autonomous systems, and mobile technology, delivering scalable and production-ready solutions that address real-world problems.