From Discrete Tokens to Continuous Vectors: The Foundation of Neural Network Learning

This chapter reveals the fundamental mechanism that allows neural networks to process text by converting discrete tokens into learnable vector representations. Understanding embeddings is essential for anyone developing AI systems, as they form the backbone of how modern neural networks capture semantic meaning. By mastering proper embedding initialization and loss functions, you'll grasp the core principles underlying contemporary language models.