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1 - 1 of 1 results for: CS 231N: Convolutional Neural Networks for Visual Recognition

CS 231N: Convolutional Neural Networks for Visual Recognition

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. Prerequisites: Proficiency in Python; CS131 and CS229 or equivalents; MATH21 or equivalent, linear algebra.
Terms: Spr | Units: 3-4
Instructors: Li, F. (PI)
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