Lecture 6 discusses the backpropagation algorithm for efficiently computing gradients of complex functions. We discuss the idea of a computational graph as a data structure for organizing computation, and show how the backpropagation algorithm allows us to compute gradients by walking the graph backward and performing local computation at each graph node. We show examples of implementing backpropagation in code using both flat and modular implementations. We generalize the notion of backpropagation from scalar-valued functions to vector- and tensor-valued functions, and work through the concrete example of backpropagating through a matrix multiply operation. We conclude by briefly mentioning some extensions to the basic backpropagation algorithm including forward-mode automatic differentiation and computing higher-order derivatives. Slides: http://myumi.ch/R58q1 _________________________________________________________________________________________________ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks. Course Website: http://myumi.ch/Bo9Ng Instructor: Justin Johnson http://myumi.ch/QA8Pg