In this module, you will learn how transformers process sequential data using positional encoding and attention mechanisms. You will explore how to implement positional encoding in PyTorch and understand how attention helps models focus on relevant parts of input sequences. You'll dive deeper into self-attention and scaled dot-product attention with multiple heads to see how they contribute to language modeling tasks. The module also explains how the transformer architecture leverages these mechanisms efficiently. Through hands-on labs, you’ll implement these concepts and build transformer encoder layers in PyTorch. Finally, you'll apply transformer models for text classification, including building a data pipeline, defining the model, and training it, while also exploring techniques to optimize transformer training performance.

In this module, you will learn how decoder-based models like GPT are trained using causal language modeling and implemented in PyTorch for both training and inference. You will explore encoder-based models, such as Bidirectional Encoder Representations from Transformers (BERT), and understand their pretraining strategies using masked language modeling (MLM) and next sentence prediction (NSP), along with data preparation techniques in PyTorch. You will also examine how transformer architectures are applied to machine translation, including their implementation using PyTorch. Through hands-on labs, you will gain practical experience with decoder models, encoder models, and translation tasks. The module concludes with a cheat sheet, glossary, and summary to help consolidate your understanding of key concepts.