Introduction to Transformer Architecture

Overview

As Generative AI applications continue to become more effective in comprehension and communicating with us, it is useful to gain a better understanding of how exactly they work.

On 11 April 2024, Arvind Devaraj conducted an online tutorial on “Introduction to Transformers”. This tutorial was aimed at providing participants with a high-level understanding of the evolution of NLP techniques to understanding the nuances of pre-trained models; from exploring RAG applications to hands-on demonstrations, this tutorial is an important resource for anyone interested in starting their journey with LLMs and the Transformer Architecture. It introduced neural networks and their applications in natural language processing (NLP).

The Transformer model (which was the focus of this session) has evolved from neural networks to handle long sequences of text. It has also led to the creation of pre-trained models like BERT, GPT, and eventually into ChatGPT.

▶️ Watch the full video to understand the inner workings of language models and see practical applications in action.

What is covered?

• Understanding Text Vectorization: Explore the process of converting text into numerical vectors, laying the groundwork for machine learning models to interpret language data.
• Linear Algebra Fundamentals: Understand the crucial aspects of linear algebra that power deep learning algorithms, including matrix multiplication and vector spaces.
• Neural Network Basics: Uncover the building blocks of neural networks, discussing their architecture, how they learn from data, and their role in advancing AI.
• Demystifying the Transformer: A comprehensive look at the Transformer model, its unique self-attention mechanism, and its revolutionary impact on natural language processing.
• Inside the Transformer Encoder (BERT): Get to grips with the BERT architecture, how it reads and understands context in text, and its breakthrough performance on language understanding tasks.
• Decoding the Transformer (GPT): Understand the GPT decoder, its generative capabilities, and how it predicts the next item in a sequence, setting new benchmarks in text generation.
• Real-World Applications of BERT and GPT: Explore practical applications where BERT and GPT are making waves, from search engines and chatbots to content creation and language translation.

Applications of Transformer models include computing embeddings (for semantic search), summarization, question answering and modern RAG applications. A demo of these applications was presented in the tutorial. There was also a brief discussion on how to implement the Transformer model in code.

👉🏽 Session slides can be viewed here.

Who should view this?

This tutorial is useful for anyone interested in understanding the fundamentals of natural language processing (NLP) and how models like ChatGPT work. This will be very useful for:

• Developers working on NLP projects such as chatbots, document understanding, information extraction.
• Product managers who want to leverage NLP and GenAI in their business workflow.
• Students / Researchers to understand the foundational deep learning methods for NLP.

Testimonials

“The instructor was well prepared and was able to tweak the presentation based on what the audience wanted. I got a good introduction to Transformer Architecture, and some of the applications starting from the very basics. Overall, I learned a lot from the content.”
Nisseem Nabar, Associate Principal at The Math Company

“Before leaping into building GenAI applications, getting the basics right is a must. Introduction to Transformer Architecture tutorial was extremely helpful as it clarified the foundational concepts.”
Dheeraj Chitlangi, Associate Vice President in Banking Sector

Special Thanks

To Sriram Srikumar for his support in organizing the venue for the tutorial session.

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