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Computer Vision Applications - Full Stack Deep Learning - Spring 2021

The Full Stack via YouTube

Overview

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This lecture reviews notable deep learning applications in computer vision, from convolutional network architectures to localization, detection, and segmentation. It also surveys advanced tasks and methods, including 3D shape inference, pose estimation, adversarial attacks, and style transfer.

Syllabus

- Introduction
- AlexNet
- ZFNet
- VGGNet
- GoogLeNet
- ResNet
- SqueezeNet
- Architecture Comparisons
- Localization, Detection, and Segmentation Tasks
- Overfeat, YOLO, and SSD Methods
- Region Proposal Methods R-CNN, Faster R-CNN, Mask R-CNN, U-Net
- Advanced Tasks 3D Shape Inference, Face Landmark Recognition, and Pose Estimation
- Adversarial Attacks
- Style Transfer

Taught by

The Full Stack

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