Welcome to DATA 2040! Within the past several years, research in the field of Deep Learning has earned state-of-the-art results on applications in computer vision (Tesla's autonomous vehicles), natural language processing (Google Translate), and deep reinforcement learning (DeepMind). This hands-on course will teach you a practical understanding of deep learning techniques under a variety of real-world problems and data types, including how to build neural networks and how to use them responsibly. You will not only learn the foundations of deep neural networks, such as common optimization methods, activation and loss functions, regularization methods, and architectures, but will also learn about convolution operations for computer vision tasks, word embeddings and recurrent neural networks for natural language processing tasks, and GANs for generative modeling tasks. With the completion of this course, you will be able to create novel neural network architectures, as well as learn how to utilize well-known deep learning models for your own tasks.
Boqing Gong (email@example.com)
Dan Potter (firstname.lastname@example.org)
Sam Watson (email@example.com)
Head TA: Aaron Wang (firstname.lastname@example.org)
TAs and Course Development Volunteers: Sayan Samanta (email@example.com)
Cang Tang (firstname.lastname@example.org)
Shiqi Lei (email@example.com)
Frank Chen (firstname.lastname@example.org)
Justin Tian (email@example.com)
Kaiwen Yang (firstname.lastname@example.org)
Shiyu Liu (email@example.com)
Connor Jordan (firstname.lastname@example.org)
Jessie Li (email@example.com)
Please sign this collaboration policy before the first assignment is due!
Remember to check piazza for announcements!