Advanced PyTorch Techniques and Applications
Master cutting-edge PyTorch applications including GANs, Transformers, and Graph Neural Networks. Perfect for ML engineers and researchers.
Master cutting-edge PyTorch applications including GANs, Transformers, and Graph Neural Networks. Perfect for ML engineers and researchers.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full PyTorch Ultimate 2024 - From Basics to Cutting-Edge Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
Powered by
What you'll learn
Implement advanced neural network architectures like GANs and Transformers
Develop recommender systems and autoencoders for real-world applications
Master Graph Neural Networks for complex data structures
Apply semi-supervised learning techniques with limited data
Deploy models efficiently using Flask and Google Cloud
Skills you'll gain
This course includes:
8.4 Hours PreRecorded video
5 assignments
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 12 modules in this course
This comprehensive course delves into advanced PyTorch applications and techniques. Beginning with recommender systems and autoencoders, students progress to complex architectures like GANs and Transformers. The curriculum covers cutting-edge topics including Graph Neural Networks, semi-supervised learning, and NLP applications. Advanced sections focus on model deployment using Flask and Google Cloud, making this course essential for professionals seeking expertise in state-of-the-art deep learning implementations.
Recommender Systems
Module 1 · 1 Hours to complete
Autoencoders
Module 2 · 23 Minutes to complete
Generative Adversarial Networks
Module 3 · 43 Minutes to complete
Graph Neural Networks
Module 4 · 46 Minutes to complete
Transformers
Module 5 · 29 Minutes to complete
PyTorch Lightning
Module 6 · 37 Minutes to complete
Semi-Supervised Learning
Module 7 · 42 Minutes to complete
Natural Language Processing (NLP)
Module 8 · 2 Hours to complete
Miscellaneous Topics
Module 9 · 1 Hours to complete
Model Debugging
Module 10 · 14 Minutes to complete
Model Deployment
Module 11 · 1 Hours to complete
Final Section
Module 12 · 1 Hours to complete
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.