Natural Language Processing: Understanding Transformers and Tokenization

This lecture covers key concepts in Natural Language Processing (NLP), focusing on the analysis and synthesis of language. It begins with an overview of previous topics, including initialization and self-supervised learning. The instructor introduces the concept of tokens, explaining how text can be broken into smaller units for processing. The lecture delves into self-attention mechanisms, illustrating how models can focus on relevant parts of the input. The architecture of transformer models is discussed, highlighting the encoder and decoder models and their roles in generating useful text representations. The encoder-decoder model is explained in the context of sequence-to-sequence tasks, such as machine translation. The lecture also addresses the importance of tokenization, detailing various methods for transforming sequences of words into vectors for deep learning applications. Finally, the instructor emphasizes the significance of understanding these concepts for developing effective NLP systems, providing insights into practical applications and future directions in the field.