This module focuses on the Computer Vision applications of deep learning.
DurationThis module focuses on the Computer Vision applications of deep learning. It covers the fundamentals of Convolutional Neural Networks and different CNN architectures, teaches image augmentation and processing using TorchVision and OpenCV, and introduces common CV tasks such as image classification, object detection, semantic segmentation, image augmentation, transfer learning, and generative models such as neural style transfer.
Learning Outcomes
Learn the fundamentals of deep convolutional neural networks
Get hands-on with various CNN architectures such as AlexNet, VGG, Inception, RestNet, and Xception
Apply CNN to solve image classification problems
Apply YOLO and R-CNN to solve object detection problems
Apply FCN and DeepLab algorithms to solve semantic segmentation problems
Learn how to label and augment image data using various tools
Key Skill:
CNN, Computer Vision, Convolutional Neural Networks, Image Augmentation, Image Classification, Object Detection, Semantic Segmentation, Instance Segmentation, and Neural Style Transfer
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