Deep Learning for Computer Vision | EECS 498/598 | University of Michigan

Comprehensive course on neural network-based deep learning methods for visual recognition tasks, including image classification and object detection.

Introduction

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.

Highlights

• Learn to implement, train and debug your own neural networks
• Gain a detailed understanding of cutting-edge research in computer vision
• Cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks

Recommendation

This course is suitable for students interested in computer vision and deep learning. It provides a comprehensive understanding of the latest advancements in neural network-based methods for visual recognition tasks.