How a 1990s textbook explains the inner workings of modern neural networks

The author shares a curious observation made while studying a 1990s linear algebra textbook. The text provides an example of representing chemical molecules as vectors, which at first glance seems unusual. However, after deep analysis, the author concludes that this approach fundamentally explains the principles of modern neural networks and the concept of representation learning. The article highlights that many modern machine learning methods have deep roots in classical linear algebra and that any subject area can be formalized for use in neural networks. The author encourages looking at mathematical foundations from a different angle, demonstrating that modern technologies are often an evolution of ideas that were taught decades ago in academia.