What is a Convolutional Neural Network?

A convolutional neural network (CNN) is a deep learning algorithm that employs image recognition, processing, and classification to identify objects and detect faces. It consists of neurons that receive inputs, assign importance to them, and cluster them according to similarities.

A CNN, also called a “ConvNet,” can look at an object’s surroundings to come up with accurate predictions. Instead of looking at the whole image to determine features, it will look at layers or smaller portions.

Other interesting terms…

• What is Self-Supervised Learning?
• What is Artificial Narrow Intelligence (ANI)?

Read More about a “Convolutional Neural Network”

Why Use a Convolutional Neural Network?

A CNN has proven highly effective in image recognition and classification, such as detecting objects, faces, and traffic signs. More recently, a CNN also effectively carried out several natural language processing (NLP) tasks, such as sentence classification. As such, a CNN could be an integral part of many machine learning (ML) applications. It assigns various levels of significance or importance to inputs for deep learning algorithms for more accurate image classification and facial recognition. Some of the reasons for its use include:

Using Same Set of Weights

Compared with traditional fully connected (FC) layers, a CNN uses filters so it can put one layer over another when analyzing an image. It uses the same set of weights (volumes assigned to measure a particular feature) to extract features from different input image areas or an object’s surroundings.

Sharing Similar Parameters

Since a CNN uses the same set of weights for inputs, it also uses the same parameters. Think about each weight as a representation of edges, curves, or textures. When processing images, using different weights for each picture would not make sense.

Resulting in Fewer Translation Variances

As a result of using the same set of weights and parameters, a CNN categorizes every image with very little variances, allowing for more accurate classification.

What Are the Applications of Convolutional Neural Networks?

Answering the question “What is a convolutional neural network?” means understanding its different applications, which include:

Image Classification

One of the most obvious applications of a CNN is image classification. It provides the most progressive frameworks, primarily in the field of medical imaging. It can deconstruct an image to identify distinguishing features. It employs supervised ML classification algorithms, making it effective in image tagging that helps with visual searches. As such, it effectively matches an input image to items in a database to identify objects with the same credentials or characteristics.

Facial Recognition

While you may think that image classification and facial recognition are the same thing, they are not. Facial recognition is much more complicated. A CNN first has to do basic object recognition, such as determining one’s face shape and other features. Beyond that, it does further analysis, which includes determining one’s nose shape, skin tone, and identifying scars or marks. All of these are then added to the image data perception and run against various samples such as when a subject is wearing sunglasses.

Social media platforms like Facebook use facial recognition to do away with the time-consuming process of manually tagging people in photos. In some cases, the technology is beneficial in verifying personal identification. While it may not be as accurate as fingerprinting, facial recognition can be used if you have limited information such as that from surveillance footage.

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Knowing the answer to “What is a convolutional neural network?” makes it easier to understand why deep learning algorithms are crucial in improving many processes, including security enhancement with facial recognition.

If you want to learn more about how CNNs work, watch this video.