Co-Instructors: Mike Keiser, Rima Arnaout, Luca Ponzoni
Course Description: This three-week mini-course will establish the foundations of practical deep learning through a hands-on approach in Python. We will cover the basics of regression and classification, the optimization and training of neural networks, and model architectures including autoencoders, convolutional neural networks, and recurrent neural networks. The primary goal of this course is to equip students with the necessary foundations to apply basic neural networks to their own research. Experience with numerical computation in Python is required (or permission from the Instructor).