Mastering Image Segmentation with PyTorch using Real-World Projects: Dive into PyTorch-Powered Image Segmentation with Real-World Projects

Bert Gollnick

$69.99

Video Sep 2023 5hrs 5mins 1st Edition

Bert Gollnick

$69.99

Video Sep 2023 5hrs 5mins 1st Edition

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Key benefits

Comprehensive coverage of PyTorch and advanced image segmentation techniques
Real-world projects focusing on practical applications in diverse domains
Step-by-step guidance on model training, evaluation, and deployment strategies

Description

Unlock the potential of image segmentation with this comprehensive course designed to merge theory with practical expertise. Begin with a strong foundation in PyTorch and image segmentation principles, setting up the environment and exploring the fundamental concepts of tensors, datasets, and neural networks. Early modules ensure a seamless transition for learners at all levels, providing clear guidance on data preprocessing and model setup. Progress into advanced topics where you'll dive deep into semantic segmentation, exploring state-of-the-art architectures such as UNet and Feature Pyramid Network. Learn about essential techniques like upsampling, loss functions, and evaluation metrics, enabling you to fine-tune models for accuracy. Through real-world projects, such as segmenting satellite images, the course emphasizes practical applications, bridging the gap between theory and industry needs. The hands-on approach culminates with model training and evaluation, focusing on metrics like pixel accuracy and Intersection over Union. You'll gain experience in setting up training loops, fine-tuning hyperparameters, and saving models for future use. By the end of the course, you will have mastered the tools and techniques necessary to tackle complex segmentation challenges confidently.

Who is this book for?

This course is tailored for data scientists, AI enthusiasts, and machine learning practitioners eager to specialize in image segmentation. Prior knowledge of Python, basic machine learning concepts, and familiarity with PyTorch is recommended.