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What is Convolutional Neural Network?

What is CNN?

It was first introduced by Yann LeCun who is a postdoctoral Computer Science Researcher. It was also called ConvNets, in the 1980s. A Convolutional Neural Network (CNN) is a form of Artificial Neural Network used largely for image identification and processing.

It is a powerful tool that can recognize patterns in images but requires millions of labeled data points for training. If CNN is to generate results rapidly enough to be effective, they must be trained with high-power processors such as a GPU or an NPU.

Even though CNNs were created to handle issues with visual imaging, they may also be used for image categorization, natural language processing, drug development, and health risk assessments. It can also assist self-driving automobiles with depth estimates.

Watch this Convolutional Neural Network Tutorial YouTube video

The following are the topics we are about to discuss further in this blog.

How Do Convolutional Neural Networks Work?

The higher performance of Convolutional Neural Networks with pictures, voice, or audio signal inputs sets them apart from conventional neural networks. As  we mentioned earlier, it is divided into three sorts of layers:

  • Convolution Layer
  • Pooling Layer
  • Fully-Connected Layer

We will further discuss these layers in detail in this blog. 

If we give an input image, it goes to convolution+Relu, each area has a 3D, RGB, then it goes to the next pooling layer where it shrinks the max value and this cycle keeps repeating. This is the learning process. We try to classify the values and then we have to apply neural nets and try to figure out what the actual image is. Given that it is a car, softmax gives a value of 0 to 1, the probability of the maximum is identified as the car.

Convolutional Neural Network Architecture

A CNN architecture is divided into two components:

  • In a process known as Feature Extraction, a convolution tool isolates and identifies the distinct characteristics of a picture for analysis. This feature extraction consists of an input, convolution layer, and pooling layer. 
  • Another component present in CNN architecture is classification in which we have fully connected the layer and output. The classification component is a fully connected layer that uses the output of the convolution process to forecast the image’s class using the information acquired in earlier stages.

The CNN becomes more complicated with each layer, detecting larger areas of the picture. Earlier layers concentrate on basic elements like colors and borders. As the visual data travels through the CNN layers, it begins to distinguish bigger components or features of the item, eventually, identifying the target object. We will talk about these layers in detail in the upcoming section.

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Convolutional Neural Network Layers

Convolutional layers, pooling layers, and fully-connected (FC) layers are the three types of layers that make up the CNN. A CNN architecture will be constructed when these layers are layered. Here is a detailed explanation of these three layers. 

Convolution layer: The convolutional layer is the most important component of a CNN since it is where most of the processing takes place. It requires input data, a filter, and a feature map, among other things. 

Let’s pretend the input is a color picture, which is made up of a 3D matrix of pixels. This implies the input will have three dimensions: height, width, and depth, which match the RGB color space of a picture. Here we try to decompose RGB to a multidimensional layer and apply a filter to each layer. 

A feature detector, also known as a kernel or a filter, will traverse over the image’s receptive fields, checking for the presence of the feature. A strider is used to stride to each matrix in the image. We try to understand these images using a convolution strider.

Pooling Layer: Pooling layers is a dimension reduction technique that reduces the number of input parameters. The pooling process sweeps a filter across the input just like the convolutional layer. However, this filter does not contain any weights, unlike the convolution layer. 

Instead, the kernel uses an aggregation function to populate the output array from the values in the receptive field. The pooling layer is also known as the Downsampling process. And, maximum pooling and average pooling are the two basic forms of pooling.

Fully-Connected Layer: The fully-connected layer’s name is a perfect description of what it is. As previously stated, with partly connected layers, the input image’s pixel values are not directly connected to the output layer. 

However, each node in the output layer links directly to a node in the preceding layer in the fully-connected layer. This layer conducts categorization based on the characteristics retrieved by the preceding layers and the filters applied to them. 

While convolutional and pooling layers often utilize ReLu functions to categorize inputs, FC layers typically use a softmax activation function to provide a probability ranging from 0 to 1.

Important aspects of CNN

The important aspects of CNN are filters, receptive field, stride, and padding. 


Filters in Convolutional Neural Networks recognize spatial patterns such as edges in an image by detecting changes in the picture’s intensity values.

Receptive Field 

Receptive fields are specified areas of space or spatial constructs that include units that offer input to a layer’s collection of units. The filter size of a layer within a Convolution Neural Network determines the receptive field.


The kernel’s stride is the number of pixels it traverses across the input matrix. Although stride values of two or more are uncommon, a bigger stride results in a lesser output.


Padding essentially increases the number of images that a convolutional neural network can handle. Each pixel is scanned by the kernel/filter as it goes over the picture, converting the image into a smaller image.

Steps to run a Convolutional Neural Networks:

  • Creating a model with MLP
  • Convolutional layer
  • Activation layer
  • Pooling layer
  • Dense (fully connected layer)
  • Model compile and train

Limitations of CNN

  • Because of operations like max pool, a Convolutional Neural Network is substantially slower.
  • If the CNN contains several layers, the training process will take a long time if the machine does not have a powerful GPU.
  • To analyze and train the neural network, a ConvNet requires a huge dataset.
  • It fails when it comes to comprehending the contents of a picture.
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Regardless of the limitations of CNNs, there’s no doubt that they’ve ushered in a new era in Artificial Intelligence. Face recognition, picture search, and editing, augmented reality, and other computer vision applications all employ CNNs today. Our results are spectacular and valuable, as improvements in CNN’s demonstrate, but we are still a long way from reproducing the core components of human intellect. We hope this blog helps you comprehend everything you need to know about convolutional neural networks.

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About the Author

Head of Data Engineering & Science

As a head of Data Engineering and Science at Chargebee, Birendra is leading a team of 50+ engineers, specializing in high-scale data and ML platforms. Previously, held key roles at Razorpay and as CTO, with extensive experience in Big Data, ML, and SAAS architecture. Recognized for 85+ contributions to tech publications and patents.