Generative AI Language Modeling with Transformers

Overview

This course covers the fundamental concepts of transformer-based models for NLP. You’ll explore the significance of positional encoding and word embedding, understand attention mechanisms and their role in capturing context and dependencies, and learn about multi-head attention. You’ll learn how to apply transformer-based models for text classification, specifically focusing on the encoder component. You will also learn about decoder-based models, such as GPT, and encoder-based models, such as BERT, and use them for language translation.

By the end of the course, you will be able to:

1. Apply positional encoding and attention mechanisms in transformer-based architectures for processing sequential data.
2. Use and implement decoder-based models (e.g., GPT) and encoder-based models (e.g., BERT) for language modeling.
3. Build a transformer model for language translation from scratch using PyTorch.

Details

Fundamental Concepts of Transformer Architecture

Positional Encoding

Understanding techniques to represent the position of tokens in sequences and implementing positional encoding using PyTorch.

Attention Mechanisms

Exploring how attention mechanisms work and their application to word embeddings and sequences.

Self-Attention Mechanisms

Learning how self-attention mechanisms aid in predicting tokens in language modeling.

Transformer Architecture

Delving into the efficiency of attention mechanisms enhanced by transformer architecture and implementing encoder layers in PyTorch.

Text Classification with Transformers

Using transformer-based models for text classification, including creating text pipelines, building models, and training them.

Advanced Concepts of Transformer Architecture

Decoder Models and GPT

Studying decoders and Generative Pre-trained Transformers (GPT) for language translation, training these models, and implementing them using PyTorch.

Encoder Models with BERT

Gaining knowledge about Bidirectional Encoder Representations from Transformers (BERT) and pretraining using Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).

Data Preparation for BERT

Performing data preparation for BERT using PyTorch.

Transformers for Translation

Understanding transformer architecture for language translation and implementing it using PyTorch.

Cheat Sheet

Language Modeling with Transformers