Andrew Ng and Kian Katanforoosh open Stanford's CS230 Deep Learning course with a survey of what the field can do and how the class will teach it. Ng runs through examples of deep learning projects, from computer vision to speech and medical applications, to establish why the subject matters before any math appears. Katanforoosh and Ng then walk through course mechanics: the syllabus, programming assignments, and how the class balances theoretical foundations with hands-on project work. The lecture is light on technical derivation, functioning as orientation to the discipline and the ten-week structure students will follow, but it comes from the instructors who built one of Stanford's most recognized machine learning sequences and gives a clear picture of what deep learning research and application look like in practice. Later lectures in the published playlist carry the technical content this one sets up.