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Learner Reviews & Feedback for Generative AI Language Modeling with Transformers by IBM
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About the Course
This course provides you with an overview of how to use transformer-based models for natural language processing (NLP).
In this course, you will learn to apply transformer-based models for text classification, focusing on the encoder component.
You’ll learn about positional encoding, word embedding, and attention mechanisms in language transformers and their role in capturing contextual information and dependencies.
Additionally, you will be introduced to multi-head attention and gain insights on decoder-based language modeling with generative pre-trained transformers (GPT) for language translation, training the models, and implementing them in PyTorch.
Further, you’ll explore encoder-based models with bidirectional encoder representations from transformers (BERT) and train using masked language modeling (MLM) and next sentence prediction (NSP).
Finally, you will apply transformers for translation by gaining insight into the transformer architecture and performing its PyTorch implementation.
The course offers practical exposure with hands-on activities that enables you to apply your knowledge in real-world scenarios.
This course is part of a specialized program tailored for individuals interested in Generative AI engineering.
This course requires a working knowledge of Python, PyTorch, and machine learning.
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1 - 4 of 4 Reviews for Generative AI Language Modeling with Transformers
By LO W
•Nov 10, 2024
Get more familiar with transformer and its application in language
By Purva T
•Jul 26, 2024
good.
By raul v r
•Oct 11, 2024
Once again, great content and not that great documentation (printable cheatsheets, no slides, etc). Documentation is essential to review a course content in the future. Alas!
By Salman M
•Oct 22, 2024
It is an excellent specialisation, except the pace of the speaker is very fast. It is difficult to understand, and it sounds very artificial.