Convolution Operation
Syllabus:
- Purpose of Convolution
- How Convolution Works
- Convolution Layers
- Filter Basics
- Weight Initialization
- Learned Filters
- Multiple Filters
(1) Purpose Of Convolution
- The purpose of convolution in the context of image processing, computer vision, and convolutional neural networks (CNNs) is to extract and process features from input data efficiently.
- It enables the model to understand patterns, spatial hierarchies, and key attributes in the data, such as edges, textures, shapes, and objects.
(2) How Convolution Works?
- Convolution is a fundamental operation in Convolutional Neural Networks (CNNs), used primarily for feature extraction.
- It allows the network to automatically learn spatial hierarchies of patterns, starting from simple features like edges and progressing to more complex structures like shapes, textures, and objects.
- Here’s how convolution works in CNNs, step by step:
(3) Example Of Convolution
