What Is An CNN?
Syllabus:
- What Is CNN?
- Differences between CNNs and fully connected networks.
- Applications of CNNs.
(1) What Is CNN?
- A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed to process and analyze grid-like data structures, such as images.
- It is widely used for tasks such as image recognition, object detection, and video analysis, among others.
(2) Difference Between CNN and ANN?
- The main difference between Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) lies in their structure and the types of tasks they excel at. Here’s a simple breakdown:
(3) What Is Spatial Information?
- Spatial relationships refer to the way objects or elements are arranged in space relative to one another.
- In the context of images and visual data, it describes how different parts of the image (like pixels, shapes, or objects) are positioned, connected, or interact with each other.
- Suppose you are classifying an image of a cat or a dog, cat will have round face, dogs will have long face, cats will have short ear dog will have long ear.
- All this information needs to be captured to classify an image.
(4) How ANN Treats Input Image As Single Flat List?
- Spatial relationships refer to the way objects or elements are arranged in space relative to one another.
- In the context of images and visual data, it describes how different parts of the image (like pixels, shapes, or objects) are positioned, connected, or interact with each other.
- Suppose you are classifying an image of a cat or a dog, cat will have round face, dogs will have long face, cats will have short ear dog will have long ear.
- All this information needs to be captured to classify an image.
- If you pass also a matrix of size 5 * 4 it will be considered as 5 records and 4 number of features.
- Hence each records will be considered as different.
- Hence we will loose the spatial information.
(3) Applications Of CNN?
Image Classification:
Object Localization:
Object Detection:
Face Recognition:
Face Recognition:
Image Segmentation:
Super Resolution:
Gray To Color Photo Conversion:
Pose Estimation:
