Large language models such as GPT-3.5, which powers ChatGPT, are changing how humans interact with computers and how computers can process text. This course will introduce the fundamental ideas of natural language processing and language modelling that underpin these large language models. We will explore the basics of how language models work, and the specifics of how newer neural-based approaches are built. We will examine the key innovations that have enabled Transformer-based large language models to become dominant in solving various language tasks. Finally, we will examine the challenges in applying these large language models to various problems including the ethical problems involved in their construction and use.
Generative Pre-trained Transformers (GPT)
Generative Pre-trained Transformers (GPT)
Taught in English
Some content may not be translated
3,717 already enrolled
4.5
(20 reviews)
Intermediate level
Recommended experience
12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
You will learn the foundations, implementations, applications, and risks of GPT.
Skills you'll gain
Details to know
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Assessments
12 assignments
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There are 3 modules in this course
This module introduces the concept of language modelling, which is the foundation of models like GPT.
What's included
6 videos1 reading4 assignments3 ungraded labs
This module describes the technical background for neural language models and an overview of how they are used to generate text.
What's included
10 videos1 reading4 assignments4 ungraded labs
This module discusses considerations that are necessary when using GPT and similar models in real-world contexts, specifically discussing the risks of using these models and approaches to mitigating these risks.
What's included
7 videos12 readings4 assignments1 ungraded lab
Instructors
Instructor ratings
4.3 (7 ratings)
Offered by
Recommended if you're interested in Machine Learning
Coursera Project Network
DeepLearning.AI
Google Cloud
Duke University
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