About this course
Computer vision (CV) is a set of techniques that are used to help machines to “see” and comprehend what is contained in digital imagery such as photos and videos. It has varied applications such as in medical imaging, motion capture, surveillance, automating retail checkouts, and in optical character recognition. This course starts with the basic steps of digitizing images, sampling them and compressing them (quantization). It then covers various methods to work with images including classification, identification, detection, etc.
Working with Images
Convolutional Neural Networks
Computer Vision Essentials
- Working with Images_Introduction
- Working with Images - Digitization, Sampling, and Quantization
- Working with images - Filtering
- Introduction to Convolutions
- 2D convolutions for Images
- Convolution - Forward
- Convolution - Backward
- Transposed Convolution and Fully Connected Layer as a Convolution
- Pooling : Max Pooling and Other pooling options
- CNN Architectures and LeNet Case Study
- GPU vs CPU
- Transfer Learning Principles and Practice
- Visualization (run pacakge, occlusion experiment)
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Discussion on Computer Vision Essentials
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