Generative AI Language Modeling with Transformers

Go to Course: https://www.coursera.org/learn/generative-ai-language-modeling-with-transformers

Introduction

Explain the concept of attention mechanisms in transformers, including their role in capturing contextual information.

Describe language modeling with the decoder-based GPT and encoder-based BERT.

Implement positional encoding, masking, attention mechanism, document classification, and create LLMs like GPT and BERT.

Use transformer-based models and PyTorch functions for text classification, language translation, and modeling.

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.

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.