Understand the history of GPT-2 and Transformer Architecture Basics
Learn the requirements for a custom training set
Learn how to use functions available in public repositories to fine-tune or train GPT-2 on custom data and generate text
In this 1-hour long project-based course, we will explore Transformer-based Natural Language Processing. Specifically, we will be taking a look at re-training or fine-tuning GPT-2, which is an NLP machine learning model based on the Transformer architecture. We will cover the history of GPT-2 and it's development, cover basics about the Transformer architecture, learn what type of training data to use and how to collect it, and finally, perform the fine tuning process. In the final task, we will discuss use cases and what the future holds for Transformer-based NLP. I would encourage learners to do further research and experimentation with the GPT-2 model, as well as other NLP models! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Intro to Transformers
Gathering a Dataset
Training and Fine Tuning our Model
Use Cases and the Future
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Great short course for those who have heard of GPT2 and are curious to get to know it better.
I enjoyed the simplicity of how things were explained.
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What is the learning experience like with Guided Projects?
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