Theory and practice of modern deep learning and associated software frameworks. A broad look at the field, drawing on material from machine vision, machine translation, dynamical systems, audio processing, neural computing and human perception. Supporting mathematical concepts are also covered, including linear algebra, stochastic optimization, and hardware acceleration. This fulfills the Intellectually Enlarging aim by requiring students to master technical "depth" and rigorous logic across diverse computational domains.