Artificial intelligence is a revolutionary technology advancing in every heartbeat, and it has evolved immensely in bridging the gap between human and machine capabilities.

Today, researchers and tech enthusiasts are working on many aspects of this field to achieve incredible things. One of many such fields is the field of computer vision.

However, numerous advancements in computer vision that happened with deep learning have developed and perfected over time, primarily through a specific algorithm – a convolutional neural network. 

• But what exactly is a convolutional neural network (CNN)?
• What are the uses and futuristic possibilities of computer vision?

Let’s find out…

What Are Convolutional Neural Networks?

First of all, let’s start with CNN. So, just as our brain identifies objects when we see an image, computers should also recognize objects similarly.

However, a huge difference exists between what a human brain sees when it looks at an image and what a computer sees. Because an image is just another set of numbers for a computer, nothing more!

However, every object has its pattern, which the computer will use to identify an object in an image.

If we explain convolutional neural networks in layman’s terms: 

Convolutional neural networks (CNNs or convnets) are a subset of machine learning. It is one of several artificial neural networks for various applications and data types. For example, a CNN is a network architecture for deep learning algorithms, especially for image recognition and pixel data processing tasks.

Now several digital giants such as Google, Amazon, Instagram, Facebook, and Pinterest are using CNNs to help grow their businesses.

Inside Convolutional Neural Networks

Artificial neural networks (ANNs) are a building block of deep learning algorithms. One type of ANN is a Recurrent neural network (RNN) that accepts sequential or time-series data as input. It is suitable for,

• Natural Language Processing (NLP)
• Language Translation
• Speech Recognition
• And Image Captioning Applications

To identify patterns within an image, a CNN uses principles of linear algebra, such as matrix multiplication. Moreover, CNN can also classify audio and signal data.

An interesting fact here is that the architecture of a CNN is similar to the connectivity pattern of the human brain!

Just as the brain has billions of neurons, CNNs have neurons arranged in a specific way.

It’s because the neurons of a CNN are arranged like the brain’s frontal lobe, the area responsible for processing visual stimuli.

This arrangement ensures that the entire field of view is covered, avoiding traditional neural networks’ piecewise image processing problem, which must provide piecewise images with reduced resolution.

Since we’ve discussed the basics of CNNs, let’s move on to a wider picture, the business applications of CNNs.

What Are The Major Business Applications Of CNNs?

Convolutional neural networks are already used in various Computer Vision and image recognition applications. However, unlike simple image recognition applications, Computer Vision also allows computer systems to extract meaningful information from visual inputs (e.g., digital images) and then take appropriate action based on that information.

The most common uses of Computer Vision and CNNs are in areas such as:

1. Healthcare: CNNs can review thousands of visual reports to detect abnormal conditions in patients, such as the presence of cancer cells.
2. Automotive: CNN technology is driving research into self-driving vehicles and self-driving cars.
3. Social media: Social media platforms use CNNs to identify the people in a user’s photo and help the user tag their friends.
4. Retail: E-commerce platforms that include visual search allow brands to recommend items that are likely to appeal to customers.
5. Audio Processing for Virtual Assistants: CNNs in virtual assistants learn and detect keywords spoken by the user and process the input to direct their actions and respond to the user.

Also Read: A Useful Guide On How To Build A Chatbot That Fits Your Business Strategy

Next, let’s go through the connection between deep learning and Convolutional Neural Networks.

As you might know, CNNs are an important pillar of Deep Learning. Let’s learn why.

Using CNN For Deep Learning

Deep learning, which has emerged as an effective tool for big data analysis, uses complex algorithms and artificial neural networks to train machines/computers to learn from experience and classify and recognize data/images like the human brain.

In deep learning, a Convolutional Neural Network is an artificial neural network widely used for recognizing and classifying images and objects. Therefore, Deep Learning uses CNNs to recognize objects in images.

Here, CNN plays an important role in various tasks and functions, such as:

• Image processing problems
• Localization and segmentation
• Video analysis
• Obstacle recognition in autonomous vehicles
• And computer vision tasks such as speech recognition in natural language processing

So, CNN’s are very prevalent in deep learning as they play an important role in this rapidly growing and emerging field.

Benefits Of Using CNNs For Deep Learning

1. For image recognition, image classification, and computer vision (CV) applications, CNNs are particularly useful because they provide highly accurate results, especially when large amounts of data are available.
2. The CNN also learns the characteristics of the object in successive iterations as the object’s data moves through multiple levels of the CNN. This helps to eliminate the need for manual feature extraction (feature engineering).
3. CNN’s ability to achieve “spatial invariance” is an important feature. In addition, it can learn to recognize and extract image features from images/data and perform extraction directly from images. This makes CNN a powerful deep-learning tool for obtaining accurate results.
4. Image analysis is the most common type of CNN; we can also use it for other data analysis and classification problems. Therefore, we can use it in various fields to achieve accurate results, including important parts such as face recognition, image classification, road/traffic sign recognition, galaxy classification, medical image interpretation, and diagnosis/analysis.

Most Popular Questions

Question: Why is CNN preferred over ANN?

Answer: They are both unique in how they work mathematically, making them better at solving certain problems. In general, CNN tends to be a more powerful and accurate way to solve classification problems. However, the ANN is still the dominant algorithm when limited data sets and image input isn’t required.

Question: What is a convolutional neural network simple explanation?

Answer: A convolutional neural network is a feed-forward neural network that analyzes visual images using a grid topology. It is also known as ConvNet. A convolutional neural network detects and classifies objects in an image.

The Future Of CNN’s

As the world evolves with every heartbeat, convolutional neural networks also open up new possibilities for humanity. Even in the simplest applications, it is impressive how much can be achieved using a neural network.

Additionally, the way CNN recognizes the images says a lot about the composition and execution of the visuals. Additionally, convolutional neural networks have discovered new medicines, one of the many inspiring examples of artificial neural networks making the world a better place.

Does CNN help us shape how we see and act in the world – remember how many times you met an interesting person because of the tag in the photo? Or how many times have you found what you were looking for via Google visual search? Those are all convolutional neural networks in action.

So as time goes by, it will get better and better. Also, this technology will surely change the world of technology and all humanity. It will also improve and enhance more like any other technology.

Author Bio

Vishnu Narayan is a dedicated content writer and a skilled copywriter working at ThinkPalm Technologies. More than a passionate writer, he is a tech enthusiast and an avid reader who seamlessly blends creativity with technical expertise. A wanderer at heart, he tries to roam the world with a heart that longs to watch more sunsets than Netflix!