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.
