Welcome to CS147! Over the past few years, Deep Learning has become a popular area, with deep neural network methods obtaining state-of-the-art results on applications in computer vision (Self-Driving Cars), natural language processing (Google Translate), and reinforcement learning (AlphaGo). These technologies are having transformative effects on our society, including some undesirable ones (e.g. deep fakes). This course intends to give students a practical understanding of how Deep Learning works, how to implement deep neural networks, and how to apply them ethically. We introduce students to the core concepts of deep neural networks, including the backpropagation algorithm for training neural networks, as well as specific operations such as convolution (in the context of computer vision) and word embeddings and recurrent neural networks (in the context of natural language processing). Throughout the lectures, labs, and assignments, we emphasize and require students to think critically about potential ethical pitfalls that can result from mis-application of these powerful models. The course is taught using the TensorFlow deep learning framework.

Professor: Daniel Ritchie (daniel_ritchie@brown.edu)
Time & Location: MWF 12:00pm – 12:50pm in Salomon DECI

Documents: Course Missive
Capstone Form: https://forms.gle/1fASZSXmkPnzvVBP8
Anonymous Feedback Form: https://forms.gle/qRd9uZnurpGdAhbb9
Piazza: https://piazza.com/brown/fall2019/csci1470csci2470/
Lecture Capture: Brown Panopto
Contact course staff: cs1470tas@lists.brown.edu
Contact HTAs + Professors: cs1470headtas@lists.brown.edu


The schedule for Deep Learning Day is now online!

Sign the collaboration policy google form by Weds, September 25th — we can't grade your work unless you sign this!