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Deep Learning for Computer Vision

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11 August 2020


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Deep Learning for Computer Vision

Abstract

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.

Lecture 1 gives a broad introduction to computer vision and machine learning. We give a brief history of the two fields, starting in the 1950s and leading up to the modern explosion of deep neural networks. We preview some of the topics we will 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.


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