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Total Size:
10.9 MB
Info Hash:
8F20D9E088CA6463EFF0608E915AB7183AAC9D03
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Added:
April 22, 2026, 2:09 a.m.
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(Last updated: April 22, 2026, 2:12 a.m.)
| File | Size |
|---|---|
| Code.zip | 481.6 KB |
| Siddiqui M. Performance Computer Vision Solutions with PyTorch 2.0 ...2025.pdf | 10.4 MB |
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12.2 MB
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2024-10-22
| Uploaded by indexFroggy | Size 12.2 MB | Health [ 16 /3 ] | Added 2024-10-22 |
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71.0 MB
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2026-03-27
| Uploaded by andryold1 | Size 71.0 MB | Health [ 23 /25 ] | Added 2026-03-27 |
NOTE
SOURCE: Siddiqui M. Performance Computer Vision Solutions with PyTorch 2.0 ...2025
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COVER
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MEDIAINFO
Textbook in PDF format 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. 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. Designed for both beginners and intermediate learners in computer vision and deep learning, this book aims to provide practical and in-depth insights into PyTorch’s powerful capabilities. The journey starts with a thorough examination of PyTorch’s past, helping readers appreciate how PyTorch’s unique architecture and foundational principles have distinguished it in the landscape of Machine Learning frameworks. This historical context will lay a solid foundation for understanding the unique benefits of PyTorch, making it a tool of choice for many researchers and practitioners in the field. 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. Preface Diving into PyTorch 2.0 PyTorch Basics Transitioning from PyTorch 1.x to PyTorch 2.0 Venturing into Artificial Neural Networks Diving Deep into Convolutional Neural Networks (CNNs) Data Augmentation and Preprocessing for Vision Tasks Exploring Transfer Learning with PyTorch Advanced Image Classification Models Object Detection Models Tips and Tricks to Improve Model Performance Efficient Training with PyTorch Lightning Model Deployment and Production-Ready Considerations
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