An exploration of the mathematical principles that make Neural Networks learn from data. Students will build different Neural Network architectures in Julia (from scratch) and in Python with tensorflow*. We will cover Feedforward, Recurrent and Convolutional Models. Models will be trained to solve Timbre detection, Genre classification, and Natural Language Processing tasks. Students develop their own original research project using Deep Learning. This course is meant for individuals who want to understand how neural networks work. Prerequisites: (1) multivariate calculus and (2) programming proficiency.