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 (email@example.com) & Eugene Charniak (firstname.lastname@example.org)
Time & Location: MWF 12:00pm – 12:50pm in Salomon DECI
Documents: Course Missive
Lecture Capture: Brown Panopto
Contact course staff: email@example.com
Contact HTAs + Professors: firstname.lastname@example.org