CSCI 1470 / 2470 Deep Learning
Fall 2021
Fall 2021
Welcome to CSCI 1470 / 2470! Deep Learning belongs to a broader family of machine learning methods. It is a particular version of artificial neural networks -- a version that emphasizes learning representation with multiple layers of networks. Deep Learning, plus the specialized techniques that it has inspired (e.g. convolutional neural networks, recurrent neural networks, and transformers), have led to rapid improvements in many applications, such as computer vision, machine learning, sound understanding, and robotics. This course intends to give students an overview of the prominent techniques of Deep Learning and its applications in computer vision, language understanding, and other areas. It also aims at providing hands-on practice of implementing deep learning algorithms in Python (e.g. with Tensorflow, Jax, or PyTorch).
Instructor: Chen Sun
HTAs: cs1470headtas@lists.brown.edu
Class time: Monday, Wednesday and Friday, 12:00-12:50pm
Classroom: TBD. Course lectures will also be made available online.
Office hours: TBD
Course syllabus: Tentative syllabus.
Learning goals: Students who complete this course will:
Learn the basic concepts and tools that power Deep Learning.
Be familiar with state-of-the-art Deep Learning technology and applications.
Grow hands-on experience developing models for vision, language, or robotics applications.
(2470-level) Develop skill to understand and implement a research paper.
Textbook: None required. Students are encouraged to refer to the following textbooks, all of which are available online:
Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Probabilistic Machine Learning: An Introduction, by Kevin Murphy.
Patterns, Predictions, and Actions: A Story about Machine Learning, by Moritz Hardt, and Benjamin Recht.
Discussion: Join Ed discussion.
Grading policy: TBD