Data Science

Convolutional Neural Network 

Posted in Data Science
Convolutional Neural Network 


In today's business world, everyone is running for success by competing with each other by providing world-class services. Earlier the competition was just taking place in the physical market, where buyers and sellers interacted with each other but today even the internet platform is also used in competing with each other by using various data science systems and the Convolutional Neural system is one of them.

Understanding Convolutional Neural Network System

From the name itself, it is very evident that the convolutional neural system is a weird combination between maths and biology and the development of this technique is the best achievement in the field of computer science. 2012 was the main year when the neural network was developed by Alex Krizhevsky because of which they win that year's ImageNet rivalry (an annual competition that was based on computer vision), in which the problem of errors was dropped from 26% to 15%, and it was one of the most important and amazing improvement at the time.

Because of all such advantages the big business firms and a large group of organizations have been utilizing this profound learning of convolutional neural systems at the centre of their administrations and in the field of computer science. There are a lot of companies that are using this technique of neural systems that are Google, Facebook, Amazon, and many more. All these big companies are using such techniques for different purposes like for automatic tagging, photo search, personalized feed, etc.

The Problem

In Convolutional Neural Network's image classification is a problem space that includes two things, the first one is the input image and the second one is the outputting an image. Outputting an image means classifying the image whether the input image is labelled correctly. For every human inputting an image means recognition of the objects that they see around them and this recognition is the first skill that every human being learns from their childhood.

They can label any object that they see around them because they are living in an environment where they have an encounter with all such objects on day to day basis and because of this they know various features of the objects by which they can make a distinction between the various objects but this art of recognizing the objects is not possible for a machine like a computer because they have never met with the objects natural surroundings. So it becomes quite difficult for a machine to recognize and differentiate between objects. This is the main problem that the convolutional neural system has to face.

The Solution

When a computer is given an input image, the first task of the computer is to convert the image into pixel values. Based on two things the computer will get an array of numbers and that two things are image size and its resolution. This process is quite meaningless for the common person but this array of numbers is the main function and working of a computer like a machine which helps it to classify the image like whether it’s a dog or a cat.

The Need for Convolutional Neural System

The convolutional neural system is used for picture handling and inside the process of picture preparation, we learn about how to investigate and how to utilize these Convolutional Neural Networks for picture arrangement. In this, we also go through picture grouping, which is the assignment where we gather the different objects for the picture and also yielding a class that includes a feline, sound, and many other things. Or it includes a likelihood of classes that best portrays the picture. For people, the recognition of various objects, scenes, etc is one of the primary abilities we gain from the minute we are conceived and it is one of the abilities that works out easily and easily as we grows-up. Without reconsidering, rapidly and consistently we are ready to distinguish the various aspects of the earth.

At the point when we see a picture of any kind of scene, we can exactly recognize and describe the scene by giving each item a name, all without even deliberately taking note. These abilities to have the option to rapidly perceive designs, sum up from earlier information, and adjust to various picture conditions are very great but such important ability is not present in the artificial machines like computers, in which they cannot classify the basic things around them. At the point when a PC sees a picture (accepts a picture as a piece of information), it will see a variety of pixel esteems. Contingent upon the goals, resolutions, and size of the pictures, it will see a 32 x 32 x 3 cluster of array numbers (The 3 alludes to RGB values).

Suppose we have a shading picture in JPG structure and its size is 480 x 480. The delegate exhibit in array number will be 480 x 480 x 3. Every single number of that image is given an incentive from 0 to 255, which portrays the pixel power to identify the image. These numbers, while good for nothing to us when we perform picture arrangement, but this array of numbers are the main data sources accessible to the PC which helps a machine in recognition of the image, and as a result, it provides a perfect output image. The thought is that you give the PC this variety of numbers and it will yield numbers that depict the likelihood of the picture being a sure class (.80 for feline, .15 for the hound, .05 for the flying creature, and so forth),

Real-Life Applications of Convolutional Neural Network 

Since we realize the issue just as the sources of information and yields, we should consider how to move toward this. What we need is that the PC is to have the option to separate and recognize between all the pictures so that it can give and make sense of the one of a kind highlights that make a canine a pooch or that make a feline a feline. This is the procedure that goes on in our brains. At the point when we take a gander at an image of a pooch, we can characterize its all elements only and only if the image has recognizable highlights, for example, paws or 4 legs.

Along these lines, the PC can do picture arrangement by searching for low-level highlights, for example, edges and bends, and afterwards developing it to progressively extract ideas through a progression of convolutional layers. This is a general are much more important in this neural system. The connection between Convolutional neural systems with other streams is: At the point when you initially knew about the term convolutional neural systems, you may have thought of something related with neuroscience or science, and it is quite obvious because the name itself includes the word neural, so you would be correct in establishing a relationship with science. Convolutional Neural networks do take a natural motivation from the visual cortex.

The visual cortex has little areas of cells that are touchy to explicit locales of the visual field. This thought was developed by an interesting analysis by Hubel and Wiesel in 1962 (Video) where they demonstrated that some individual neuronal cells in the mind reacted (or terminated) just within the sight of edges of a specific direction. For instance, a few neurons terminated when presented to vertical edges and some when demonstrated level or inclining edges. Hubel and Wiesel discovered that these neurons were sorted out in a columnar design and that together, they had the option to deliver visual recognition.

This thought of particular segments within a framework having explicit undertakings (the neuronal cells in the visual cortex searching for explicit qualities) is one that machines use too and is the premise behind Convolutional Neural Networks.

Structure of Convolutional Neural Network 

As from the above information, it is quite clear that the of identification of the different images is a very complex process for a machine, in which CNN's make use of input image and pass that image through various series of convolutional, nonlinear, pooling and fully connected layers and at last after classifying the image we get the output and this output can be a single class or the probability of classes that best classifies and describes the image. There are a lot of different layers in the process of identification. The first one is the math part, the second is a high-level perspective and the last one is a fully connected layer. All such layers play an important part in the process of classification of an image by a machine like a computer.


When the firm uses this data science technique they get a lot of benefits like:

1. Convolutional Neural Network can easily capture any kind of image and it also helps the firm to reveal the main features of an input image. Because of this Convolutional Neural Network's, a computer-like machine can identify any kind of image very easily without any problems.

2. Convolutional Neural Network also provides the benefit of weight sharing, this means that it is more efficient in handling a large amount of data and it has billions of neurons that can handle any complexity.

3. It is very accurate in the performance and it is very speedy in its workings of identifying the input image whether the image is of dog or cat.

4. It has very high statistical and computational efficiency.


1. The convolutional neural system is very complicated to understand and to learn because the working of this system includes a lot of layers that are related to convolutional layers.

2. There are layers used in identifying various input images and this process is time-consuming also.


Object detection, recognition, and classification are powered by deep neural architectures. The architectures differ from each other in terms of parameters such as activation functions, filters, number of layers, and processing speed. In this article, we learned in detail about various layers of Convolutional Neural Network s that helps the computer to handle the image input and also helps in its identification. Here we also discussed various advantages that the convolutional neural system is providing to its user. 

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Simran Kaur Arora

Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her. View all posts by the Author

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