This repository contains my solution to the assignments of the "EECS 498-007 / 598-005 Deep Learning for Computer Vision" course made by Michigan University and Justin Johnson Fall 2020. All the course materials like slides, lecture notes, additional readings are available in the course schedule webpage.
Also, you can download the original starter codes regarding the course assignments (without my solution) via the following links:
You can watch the course lectures on YouTube. Here is the official playlist.
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.
Big fat thanks to Michigan University for making this great course publicly-available.