Generative AI Language Modeling with Transformers
Master transformer architecture for NLP tasks with hands-on PyTorch implementation.Generative AI Language Modeling with Transformers
Master transformer architecture for NLP tasks with hands-on PyTorch implementation.Generative AI Language Modeling with Transformers
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 Generative AI Engineering with LLMs 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:
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What you'll learn
Implement attention mechanisms and positional encoding
Develop transformer models for text classification
Train GPT-like models for language translation
Master BERT pretraining with MLM and NSP
Create PyTorch implementations of transformers
Optimize transformer architectures for NLP tasks
Skills you'll gain
This course includes:
1.77 Hours PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 2 modules in this course
This comprehensive course covers the fundamentals and advanced concepts of transformer architecture for natural language processing. Students learn about positional encoding, attention mechanisms, and transformer implementations using PyTorch. The curriculum includes both encoder-based (BERT) and decoder-based (GPT) models, with hands-on practice in text classification, language translation, and model training. Through practical labs, learners gain experience in implementing attention mechanisms, preparing data, and fine-tuning transformer models.
Fundamental Concepts of Transformer Architecture
Module 1 · 3 Hours to complete
Advanced Concepts of Transformer Architecture
Module 2 · 5 Hours to complete
Fee Structure
Instructors
Ph.D. Candidate in Health Informatics and Data Scientist at IBM
Fateme is a fourth-year Ph.D. candidate in Health Informatics at McMaster University, where she specializes in applying machine learning to detect behavior abnormalities in sensor data streams. In addition to her academic work, she is a data scientist at IBM. Fateme has published research in esteemed journals like ACM Transactions of Computing for Healthcare and has presented her work at leading institutions, including Mayo Clinic and the Duke Center for Health Informatics. Her contributions are advancing the field of data-driven healthcare solutions.
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
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