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Generative AI Language Modeling with Transformers

Joseph Santarcangelo, Fateme Akbari, and Adrian Wang

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.

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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What's inside

Syllabus

Fundamental Concepts of Transformer Architecture

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.

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what should give you pause

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Examines transformer-based models, which are widely used in industry for NLP

Taught by experienced instructors who are recognized for their work in NLP

Provides hands-on labs for practical exposure to NLP tasks

Builds a foundation in transformer architecture for beginners in NLP

Requires working knowledge of Python, PyTorch, and machine learning, which may pose a barrier to entry for some learners

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Reviews summary

In-depth generative ai with transformers

According to students, this course offers a deep dive into transformer models, providing a strong balance of theory and practical PyTorch implementation. Learners found the coverage of concepts like attention mechanisms, BERT, and GPT models to be exceptionally clear and comprehensive. The hands-on labs are highlighted as particularly useful for reinforcing understanding and applying knowledge in real-world scenarios. However, many learners emphasize that the course assumes a very strong background in Python, PyTorch, and machine learning, noting a rapid pace and sometimes insufficient guidance in labs for less experienced individuals, making it not suitable for beginners.

Complex topics like attention mechanisms are well-explained.

"The way they explained attention mechanisms and self-attention was brilliant."

"The explanations of positional encoding and different attention mechanisms were spot on."

"The instructors did an excellent job of simplifying complex topics."

Hands-on activities are valuable for practical application.

"The hands-on labs were incredibly useful for solidifying my understanding of concepts like positional encoding and multi-head attention."

"The practical examples using PyTorch were relevant and well-structured. I feel much more confident in applying these models now."

"The PyTorch implementations were concise and directly applicable. The labs reinforced the learning effectively."

Provides a thorough understanding of transformer models.

"This course provided an excellent deep dive into transformer models, covering both theory and practical implementation in PyTorch."

"Absolutely fantastic! This course clarified so many concepts about generative AI and transformers that I found confusing elsewhere."

"Exceptional content and well-structured. My understanding of BERT and GPT models has significantly improved."

"This course provided a thorough understanding of transformer models, from foundational concepts to advanced architectures like BERT and GPT."

Lab guidance and depth met mixed expectations.

"I felt some parts, especially the PyTorch implementations, could have been explained in more detail for someone who isn't already an expert."

"The labs were challenging, sometimes frustrating due to insufficient guidance. I had to supplement a lot with external resources."

"The labs felt a bit basic for the level of prerequisites mentioned. I found myself wanting more challenging problems."

"I learned a lot, particularly about the nuances of encoder-decoder architectures, though the exercises sometimes assumed too much prior knowledge."

Requires strong Python, PyTorch, and ML background.

"It assumes a very strong background in PyTorch and machine learning, which, while stated, felt more demanding than expected."

"The prerequisites are no joke – you really need to be comfortable with PyTorch."

"The course is definitely not for beginners. The pace is fast, and if your PyTorch skills aren't top-notch, you'll struggle."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI Language Modeling with Transformers with these activities:

Read and summarize technical documentation

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Prepare yourself for the course by reviewing materials that will establish a strong foundation

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Identify key concepts and definitions in the material
Use active recall techniques to assist with retention

Review Python programming basics

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Ensure you have a solid foundation in Python programming

Browse courses on Python

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Review online tutorials or documentation
Solve practice problems and coding challenges
Build a small Python project

Review mathematical concepts

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Recall the foundational mathematical concepts necessary to succeed in the course

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Solve practice problems to refresh your understanding
Attend online forums or discussion groups to clarify concepts
Review textbooks or online resources to supplement your learning

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Join a study group

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Engage with peers to foster a deeper understanding of the course material

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Find a study group or start your own
Meet regularly to discuss the course material
Work together on assignments and projects
Quiz each other and provide feedback

Follow online tutorials on transformer models

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Supplement your learning with guided tutorials from experts

Browse courses on Transformer Architecture

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Find reputable online tutorials on transformer models
Follow the tutorials step-by-step
Complete the exercises and assignments
Apply what you've learned to your own projects

Design a transformer model

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Reinforce your understanding of transformer models by creating your own design

Browse courses on Transformer Architecture

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Research different types of transformer models
Identify the specific problem you want to solve
Design the architecture of your model
Implement your model in a programming language
Test and evaluate your model

Attend industry conferences

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Connect with professionals in the field to gain insights and learn about the latest trends

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Research industry conferences related to transformer models
Register for and attend the conferences
Network with attendees and speakers
Learn about the latest advancements in the field

Create a tutorial on transformer models

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Enhance your understanding by explaining the concepts to others

Browse courses on Transformer Architecture

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Identify the key concepts you want to cover
Organize your material in a logical flow
Create visual aids to illustrate your points
Record or write your tutorial
Share your tutorial with others

Career center

Learners who complete Generative AI Language Modeling with Transformers will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Generative AI Language Modeling with Transformers.

Speech and Language Processing. An Introduction to...

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This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.

Generative AI Language Modeling with Transformers

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What's inside

Syllabus

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Reviews summary

In-depth generative ai with transformers

Activities

Career center

Reading list

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