With the advancements in technology, artificial intelligence (AI) has become a regular part of our lives.
From virtual assistants to autonomous vehicles, AI is being used across industries and in various applications.
The rise of artificial intelligence is particularly impressive in certain fields, including computer vision.
What Are Convolutional Neural Networks (CNN)?
Convolutional neural networks, or CNNs, are powerful tools for deep learning.
They have been used to achieve state-of-the-art performance in computer vision tasks such asdetecting objects and recognizing faces.
At their core, convolutional neural networks (CNNs) are composed of neurons with adjustable weights and biases.
These neurons are organized into layers that perform specific tasks.
Each layer is connected to the previous layer, with the input layer receiving information from the outside world.
How Do Convolutional Neural Networks Work?
These convolutions enable them to extract meaningful features from images or sound waves quickly and accurately.
Convolution involves multiplying each element of an image matrix with another matrix called a filter.
As a result, convolutional neural networks continue to get smarter as more data is fed into the system.
They utilize variousmachine learning algorithmsto speed up computations and improve accuracy when it comes to object detection.
Here are five popular applications of convolutional neural networks.
To do this, they must be trained using thousands of images containing faces from various angles and expressions.
Documentation Analysis
CNNs offer numerous advantages over conventional rule-based systems when used for analyzing documents.
For instance, they require much less effort than other techniques since limited human intervention is needed.
Biometric Authentication
Biometric authentication technology, such asfingerprint scanners, has evolved considerablyover the past decade.
CNNs are being used not just for workplace safety but also for marketing in the retail and automotive industries.
