Mastering Image Segmentation with PyTorch
Learn practical image segmentation using PyTorch. Master CNNs, semantic segmentation, and deep learning techniques for real-world applications.
Learn practical image segmentation using PyTorch. Master CNNs, semantic segmentation, and deep learning techniques for real-world applications.
This comprehensive course offers a deep dive into image segmentation using PyTorch, combining theoretical foundations with hands-on implementation. Starting with essential concepts, students progress through PyTorch basics, CNNs, and advanced semantic segmentation techniques. The curriculum emphasizes practical application, featuring real-world projects and industry-standard evaluation metrics. Led by experts, the course covers everything from data preparation to model architecture, making complex segmentation tasks accessible to both beginners and experienced practitioners. The hands-on approach ensures students can confidently implement these techniques in real-world scenarios.
Instructors:
English
Powered by
What you'll learn
Apply multi-class semantic segmentation using PyTorch to real-world datasets
Analyze UNet and FPN model architectures for effective image segmentation
Implement and optimize deep learning models with appropriate loss functions
Master CNN architecture and layer calculations for image analysis
Develop practical skills in data preparation and preprocessing
Evaluate model performance using industry-standard metrics
Skills you'll gain
This course includes:
303 Minutes PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This comprehensive course covers the fundamentals and advanced techniques of image segmentation using PyTorch. Students begin with PyTorch basics and tensor operations before progressing to convolutional neural networks (CNNs) and semantic segmentation. The curriculum includes hands-on coding sessions for data preparation, model building, and evaluation. Key topics include CNN architecture, upsampling methods, loss functions, and performance metrics. The course emphasizes practical implementation through real-world projects and industry-standard evaluation techniques.
Course Overview and Setup
Module 1 · 35 Minutes to complete
PyTorch Introduction (Refresher)
Module 2 · 1 Hours to complete
Convolutional Neural Networks (Refresher)
Module 3 · 49 Minutes to complete
Semantic Segmentation
Module 4 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Enhancing IT Education Through Expert-Led Learning
Packt Course Instructors are dedicated to delivering high-quality educational content across a wide range of IT topics, offering over 5,000 eBooks and courses designed to improve student outcomes in technology-related fields. With a focus on practical knowledge, instructors leverage their industry expertise to create engaging learning experiences that help students grasp complex concepts and apply them effectively. The courses cover diverse subjects, from programming languages to advanced data analysis, ensuring that learners at all levels can find relevant resources to enhance their skills. Additionally, Packt emphasizes personalized learning paths and provides analytics tools for educators to monitor student engagement and success, making it a valuable partner in academic settings.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.