MIT's 6.7960 Deep Learning course opens with instructor Sara Beery laying out what the semester covers and introducing neural networks from the ground up. She walks through the basic building blocks, neurons, layers, weights, and activation functions, and explains how these pieces combine into the architectures that power modern machine learning systems. The lecture runs 61 minutes and sets expectations for the course structure, prerequisites, and the kinds of problems deep learning is applied to, from vision to language. Rather than diving into heavy mathematics immediately, Beery frames the intuition behind why deep networks work before the course moves into more technical territory in later sessions. It functions as an orientation lecture, giving students without prior exposure enough grounding to follow the architectures and training methods covered in subsequent weeks.