Lecture 1 provides a broad introduction to computer vision and machine learning. We provide a brief history of the two fields, starting in the 1950s and leading to the modern explosion of deep neural networks. We'll preview some of the topics we'll cover in the rest of the course, and discuss the enormous potential of deep learning and computer vision to improve our lives. We also discuss the logistics and philosophy of this course.

Slides: http://myumi.ch/yKgM3
________________________________________________________________________________________________

Computer vision has become ubiquitous in our society, with applications in search, image understanding, apps, maps, medicine, drones and self-driving cars. At the heart of many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have significantly improved the performance of these advanced visual recognition systems. This course is a deep dive into the 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 cover learning algorithms, neural network architectures, and practical technical tricks for training and tuning networks for visual recognition tasks.

Course website: http://myumi.ch/Bo9Ng

Instructor: Justin Johnson http://myumi.ch/QA8Pg

Please take the opportunity to connect and share this video with your friends and family if you find it helpful.