This course provides a practical introduction to using transformer-based models for natural language processing (NLP) applications. You will learn to build and train models for text classification using encoder-based architectures like Bidirectional Encoder Representations from Transformers (BERT), and explore core concepts such as positional encoding, word embeddings, and attention mechanisms.
Generative AI Language Modeling with Transformers
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Generative AI Language Modeling with Transformers
This course is part of multiple programs.
Instructors: Joseph Santarcangelo +2 more
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4.5
(82 reviews)
Intermediate level
Recommended experience
8 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Explain the role of attention mechanisms in transformer models for capturing contextual relationships in text
Describe the differences in language modeling approaches between decoder-based models like GPT and encoder-based models like BERT
Implement key components of transformer models, including positional encoding, attention mechanisms, and masking, using PyTorch
Apply transformer-based models for real-world NLP tasks, such as text classification and language translation, using PyTorch and Hugging Face tools
Skills you'll gain
- Category: Generative AI
- Category: Applied Machine Learning
- Category: Text Mining
- Category: Deep Learning
- Category: Natural Language Processing
- Category: Large Language Modeling
- Category: PyTorch (Machine Learning Library)
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Assessments
6 assignments
Taught in English
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There are 2 modules in this course
In this module, you will learn the techniques to achieve positional encoding and how to implement positional encoding in PyTorch. You will learn how attention mechanism works and how to apply attention mechanism to word embeddings and sequences. You will also learn how self-attention mechanisms help in simple language modeling to predict the token. In addition, you will learn about scaled dot-product attention mechanism with multiple heads and how the transformer architecture enhances the efficiency of attention mechanisms. You will also learn how to implement a series of encoder layer instances in PyTorch. Finally, you will learn how to use transformer-based models for text classification, including creating the text pipeline and the model and training the model.
What's included
6 videos4 readings2 assignments2 app items1 plugin
In this module, you will learn about decoders and GPT-like models for language translation, train the models, and implement them using PyTorch. You will also gain knowledge about encoder models with Bidirectional Encoder Representations from Transformers (BERT) and pretrain them using masked language modeling (MLM) and next sentence prediction (NSP). You will also perform data preparation for BERT using PyTorch. Finally, you learn about the applications of transformers for translation by understanding the transformer architecture and performing its PyTorch Implementation. The hands-on labs in this module will give you good practice in how you can use the decoder model, encoder model, and transformers for real-world applications.
What's included
10 videos6 readings4 assignments4 app items2 plugins
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Instructors
Instructor ratings
4.1 (13 ratings)Joseph Santarcangelo
IBM
35 Courses1,966,408 learners
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IBM
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Learner reviews
4.5
82 reviews
- 5 stars
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- 3 stars
3.61%
- 2 stars
1.20%
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Showing 3 of 82
AB
5
Reviewed on Dec 29, 2024
This course gives me a wide picture of what transformers can be.
RR
4
Reviewed on Oct 10, 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!
VB
4
Reviewed on Nov 16, 2024
need assistance from humans, which seems lacking though a coach can give guidance but not to the extent of human touch.
Frequently asked questions
It will take only two weeks to complete this course if you spend 3–5 hours of study time per week.
It would be good if you had a basic knowledge of Python and a familiarity with machine learning and neural network concepts. It would be beneficial if you are familiar with text preprocessing steps and N-gram, Word2Vec, and sequence-to-sequence models. Knowledge of evaluation metrics such as bilingual evaluation understudy (BLEU) will be advantageous.
This course is part of the Generative AI Engineering Essentials with LLMs PC specialization. When you complete the specialization, you will prepare yourself with the skills and confidence to take on jobs such as AI Engineer, NLP Engineer, Machine Learning Engineer, Deep Learning Engineer, and Data Scientist.