Introduction to Transformer Models for NLP: Unit 1

Overview

This course covers the development of natural language processing (NLP), starting with basic concepts and moving to modern transformer architectures. You will learn about attention mechanisms and their impact on language modeling, as well as the details of transformer models, including scaled dot product attention and multi-headed attention. The course includes practical exercises in transfer learning using pre-trained models such as BERT and GPT, with instruction on fine-tuning these models for specific NLP tasks in PyTorch. By the end, you will understand the theory behind current NLP models and gain practical experience in applying them to real-world problems.

Syllabus

Introduction to Transformer Models for NLP: Unit 1

This module explores the evolution of natural language processing (NLP) through the development and application of attention mechanisms and transformer architectures. Beginning with the history and foundational concepts of attention in language models, it delves into the transformative impact of transformers and their unique attention mechanisms. The module concludes with practical instruction on transfer learning, demonstrating how to fine-tune state-of-the-art pre-trained models like BERT and GPT using PyTorch to achieve advanced NLP results.