Part 1 The rise of foundation machine learning models

This part gently introduces the concept of foundation models. In chapter 1, we explore the transformer architecture from a time-series forecasting perspective; this deep learning architecture powers most of the foundation models we explore in the following chapters. We also highlight the benefits and drawbacks of foundation models. In chapter 2, we experiment with the fundamental concepts of foundation models—pretraining, transfer learning, and fine-tuning—by building our own tiny foundation model and realizing how big a challenge it is.