Rama Ramakrishnan opens MIT's 15.773 Hands-On Deep Learning course with a survey of how the field arrived at its current form, tracing the historical arc from early neural network research through the deep learning resurgence. He frames the course's practical, applied orientation before getting into substance: the building blocks of neural networks and the mechanics of training deep models. The 57-minute session covers the vocabulary and intuition needed before hands-on work begins, including why deeper architectures became trainable and useful, and what problems arise when optimizing them. As a first lecture it sets expectations and terminology rather than working through detailed derivations, but it gives a clear map of where the course is headed and why the topic matters for practitioners building real systems.