Generative Pre-trained Transformers (GPT)
Learn the fundamentals of GPT and large language models, including technical foundations, implementations, applications, and ethical considerations.
Learn the fundamentals of GPT and large language models, including technical foundations, implementations, applications, and ethical considerations.
This comprehensive course explores the foundations of GPT and large language models, covering essential concepts in natural language processing and language modeling. Through a combination of theoretical understanding and hands-on Python labs, students learn about transformer architectures, neural language models, and text generation techniques. The curriculum addresses both technical implementation and critical ethical considerations, including risks and mitigation strategies. Participants gain practical experience with building blocks of transformers while developing a deep understanding of how modern language models work.
4.6
(27 ratings)
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What you'll learn
Master language modeling fundamentals
Understand transformer architecture and GPT functionality
Implement neural language models with Python
Evaluate and generate text using transformers
Assess ethical implications of language models
Apply GPT models to real-world problems
Skills you'll gain
This course includes:
134 Minutes PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
The course is structured across three comprehensive modules covering the fundamentals of language modeling, transformer architectures, and practical applications. Starting with basic concepts of language models, students progress through neural approaches and transformer architectures, culminating in real-world applications and ethical considerations. The curriculum combines theoretical knowledge with hands-on Python labs to provide practical experience with language model implementation and evaluation.
Language Modeling
Module 1 · 4 Hours to complete
Transformers and GPT
Module 2 · 3 Hours to complete
Applications and Implications
Module 3 · 4 Hours to complete
Fee Structure
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Instructors
Expert in Machine Learning and AI Technologies
Jake Lever serves as a faculty member at the University of Glasgow, where he specializes in teaching and researching artificial intelligence, with a particular focus on Generative Pre-trained Transformers (GPT). He leads the course "Generative Pre-trained Transformers (GPT)" on Coursera, which provides students with comprehensive knowledge about the architecture, capabilities, and applications of GPT models. The course covers fundamental concepts of transformer architectures, natural language processing, and the practical applications of large language models. His teaching approach combines theoretical understanding with hands-on implementation, helping students grasp both the technical aspects and practical implications of GPT technology. Through this course, he guides learners through the complexities of modern AI systems, including model training, fine-tuning, and responsible AI implementation practices.
Expert in Leadership Education and Data-Driven Management
Dr. Mary Ellen Foster is a Senior Lecturer in the School of Computing Science at the University of Glasgow, where she has been advancing the field of human-robot interaction since 2015. With a PhD from the University of Edinburgh (2007) and extensive experience at institutions including Technical University of Munich and Heriot-Watt University, she has established herself as a leading expert in social robotics and embodied conversational agents. Her research has garnered over 2,800 citations, particularly focusing on developing robots capable of natural, face-to-face conversation with humans. She has led significant projects including the European Horizon 2020 MuMMER project on socially aware human-robot interaction and is currently coordinating a UK/Canada collaborative project investigating the use of socially intelligent robots in pediatric emergency rooms. Her work spans the development of artificial characters that can both produce and understand social signals, including speech, facial expressions, body language, and gestures, contributing substantially to the field through numerous publications in prestigious venues and conferences. Her research methodology combines observational studies of human behavior, computational modeling, and practical implementation in robotic systems, making her a key figure in advancing the integration of social robots in real-world applications
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4.6 course rating
27 ratings
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