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). This course intends to give students a practical understanding of the field of Deep Learning, through lectures and labs covering both the theory and application of neural networks to the above areas (and more!). We introduce students to the core concepts of Deep Neural Networks, including the backpropagation algorithm for training neural networks, as well as specific operations like convolution (in the context of computer vision), and word embeddings and recurrent neural networks (in the context of natural language processing). We also teach the Tensorflow Framework for the expression of deep neural network models.

Professors: Daniel Ritchie (daniel_ritchie@brown.edu) & Eugene Charniak (eugene_charniak@brown.edu)
Time & Location: MWF 12:00pm – 12:50pm in Salomon DECI
Documents: Course Missive
Piazza: https://piazza.com/class/jm9zn3s938s4ux
Lecture Capture: Brown Panopto
Contact course staff: cs1470tas@lists.brown.edu
Contact HTAs + Professors: cs1470headtas@lists.brown.edu


NEW LAB POLICY! Please review the updated lab policy posted on Piazza or on the missive.

iClicker Cloud attendance will begin Friday, 9/21. This is what's used to calculate the "In-Class Participation" portion of your grade (see the course missive). If you've never used iClicker Cloud before, instructions on setting it up can be found here: https://ithelp.brown.edu/kb/articles/iclicker-cloud-reef-instructions-for-students.

Sign the collaboration policy google form by Friday, September 21st — we can't grade your work unless you sign this!

The textbook, Introduction to Deep Learning by Eugene Charniak, is available at the Brown Bookstore and at the Rock on reserve.