Unleashing the Power of 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.

Who is this book for?
This book is tailored for students, professionals, researchers, and AI enthusiasts keen to explore Computer Vision with PyTorch 2.0. A basic understanding of Python and machine learning concepts is required. Familiarity with neural networks will enhance the learning experience.

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

M. Arshad Siddiqui is 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. 

Arshad’s technical achievements span the full spectrum of AI innovation. He has designed and optimized AI pipelines to operate efficiently in resource-constrained environments, reduced deployment costs without compromising performance, and deployed advanced computer vision solutions on edge devices such as Android and iOS smartphones. His expertise also includes creating scalable architectures for large-scale AI systems and seamlessly integrating them into production workflows. 

In healthcare, he has developed AI-powered diagnostic tools to analyze medical images, aiding early detection and improving treatment outcomes. In retail, he has implemented systems for inventory management and customer analytics. His work in autonomous systems includes designing vision algorithms for self-driving vehicles and drones, focusing on real-time object detection and navigation. 

Having worked closely with startups to accelerate innovation and with Fortune 500 companies to scale AI systems, Arshad focuses on bridging the gap between cutting-edge research and practical deployment. 

In this book, Arshad shares his extensive expertise, offering readers insights into PyTorch 2.0 and its applications in computer vision, empowering them to create impactful AI solutions for dynamic, real-world challenges. 

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ABOUT TECHNICAL REVIEWERS

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Gursimar Singh is a Senior AI Scientist specializing in Computer Vision, with over six years of experience in research and development. He has been pivotal in developing and deploying advanced computer vision solutions across various industries, including agriculture, automotive and real estate photography, thermal imaging, and video conferencing. His innovative work also extends to generative AI applications for portrait animations. 

An active member of the OpenCV organization, Gursimar is contributing to the development of OpenCV5, set for release in 2025. His expertise spans algorithm development, artificial intelligence, computer vision, image processing, and generative AI. He has published a research paper and holds a pending U.S. patent in computational photography, highlighting his commitment to advancing technological frontiers. 

Gursimar is deeply passionate about education and advocates for access to quality learning and skill development. He is passionate about disseminating knowledge in AI and computer vision, helping to empower others in these rapidly evolving f ields. 

Nehaa Bansal is a trailblazing thought leader and data scientist with a deep passion for early innovation. Her extensive experience across diverse sectors including banking, finance, telecom, and insurance has honed her expertise in developing impactful predictive models. She holds top honors in her computer science bachelor's degree and a master's in data science from BITS Pilani, providing a strong foundation for her career. 

Nehaa's professional life is guided by core values of ownership, prioritizing people, and understanding the "why" behind every action. She embraces an agile approach, acting quickly, learning from failures, iterating continuously, and prioritizing fairness. Driven by a passion for solving user problems, Nehaa combines her expertise in analytics, product strategy, and leadership to develop innovative solutions that consistently push boundaries. Her unwavering dedication to continuous learning, inclusivity, and collaboration, combined with her dedication and empathy, makes her an inspiring leader driving positive change in technology and data science.