What are Convolutional Neural Networks?

Convolutional neural networks (CNNs) represent category of synthetic neural networks that possess the capability to handle organized arrays of information like pictures, dialogue or sound waves. These networks find extensive application in realm of computer sight and handling of natural language, given their capacity to adeptly grasp intricate formations and characteristics from incoming data.

Interested in understanding how Natural Language Processing and Machine Learning can be used together to achieve even greater results. Discover more in our guide Is Natural Language Processing Machine Learning.

How do convolutional neural networks work?

Convolutional neural networks apply series of filters, kernels and input data. Each filter slides over input and produces feature map, representing the input that highlights the presence of certain features. For example, a filter might detect an image’s edges, corners, colors or shapes. Feature maps are then passed to the next layer of network, where more filters are applied to extract higher level features. For example, filter might combine edges to form parts or parts of objects to form whole objects. Process is repeated until the final layer of the network, which is usually a fully connected layer that performs classification or regression tasks.  

Convolutional Neural Networks

Building Blocks of CNN Architecture

Main building blocks of CNN architecture are:

Convolutional layer: This is where the filters are applied to the input or previous layer’s output. Convolutional layer can have multiple filters, each producing different feature map. Convolutional layer can also have parameters like stride, which is the number of pixels the filter moves each time and padding, which is the number of pixels that are added to the input to preserve its size.

Pooling layer: This layer reduces size and complexity of the feature maps by applying pooling operations, like max pooling or average pooling. Pooling operation takes a small feature map region and outputs a single value summarising that region. For example, max pooling outputs maximum value in the region, while average pooling outputs the mean value. Pooling layer helps to reduce the computational cost and prevent overfitting by discarding some information.

Fully connected layer: Layer connects all neurons from the previous layer to output layer. A fully connected layer performs the final classification or regression task using a softmax or linear activation function. Fully connected layer can also have loner, regularisation technique that irregularly drops out some neurons during training to prevent overlearned.  

CNNs vs Neural Networks

CNNs are particular feedforward neural networks with fewer weights than fully connected networks. In a fully connected network, every node in one layer is connected to every node in the next, which can result in many parameters and high computational costs. In a CNN, each node in one layer is only connected to a small region of nodes in next layer, which corresponds applying filter to that region. This reduces the number of parameters and allows the network to learn local features invariant to translation.  

CNNs vs Neural Networks

Benefits Of Using CNNs For Deep Learning

One of most popular and influential types neural networks for deep learning is convolutional neural network.

CNN is type of neural network consisting of multiple layers of neurons that process input data ranked. First layer, called input layer, receives raw data, Like an image or text. Subsequent layers, called hidden layers, apply mathematical operations Like convolution, pooling and activation to extract features from the input data. Last layer, called output layer, produces final results, Like class labels or probability scores.

  • CNNs for deep learning is that they can automatically learn features from the data, without requiring manual feature engineering or domain knowledge. This makes them more efficient and adaptable than traditional machine learning methods that rely on handcrafted features and predefined rules.
  • CNNs for deep learning is that they can exploit spatial structure and locality of the data, especially for image and video data. Using convolutional filters that slide over input data, CNNs can capture local patterns and dependencies among the pixels or words. This allows them to preserve spatial information and reduce dimensionality of the data.
  • CNNs for deep learning is that they can achieve high accuracy and generalization performance on various tasks and domains. CNNs have been shown to outperform other methods on many benchmark datasets and challenges, Like ImageNet, MNIST, CIFAR 10, and more. CNNs can also be easily transferred and fine tuned to new tasks and domains, by reusing learned features and adjusting the output layer.

Types of Convolutional Neural Networks

Some types of convolution neural network have been developed for different works and domains. Some examples are:

LeNet: This is one of the earliest CNNs designed for handwritten digit recognition. It has five layers: two convolutional layers followed by average pooling layers and two fully connected layers. 

AlexNet: This is one of the first CNNs that achieved breakthrough performance on image classification. It has eight layers: five convolution neural network layers followed by max pooling layers and three fully connected layers. It also uses dropout and ReLU activation functions. 

VGGNet: This is CNN that improved image classification performance using smaller filters (3×3) and deeper networks (up to 19 layers). It has two types: VGG16 and VGG19. 

ResNet: This is CNN that introduced residual connections solve the problem of vanishing gradients in intense networks (up to 152 layers). It has four types: ResNet18, ResNet34, ResNet50 and ResNet101. 

U-Net: This is CNN that was designed for biomedical image segmentation. It has U Shaped architecture that consists of a contracting path and an expansive path. Contracting path has four convolutional blocks, each followed by max pooling, while the expansive path has four convolutional blocks, each followed by upsampling. Network also has skip connections that concatenate feature maps from contracting path to the corresponding feature maps from the expansive path. 

Transformer: This is CNN designed for natural language processing, especially machine translation. It does not use recurrent or convolution neural network layers, but instead relies on self attention mechanisms to encode and decode the input and output sequences. It has six types: Transformer Base, Big, Transformer XL, BERT, GPT-2 and GPT-3. 

Types of Convolutional Neural Networks


Convolutional neural networks are powerful and versatile types of artificial neural network that can process structured arrays of data like images, speech or audio signals. They work by applying series of filters to the input data and extracting hierarchical features that are useful for final task. They have many benefits for deep learning applications, like learning feature engineering by themselves, handling high dimensional and complex data, achieving state of the art performance on many tasks and being easily adaptable and extendable. Many types are developed for different tasks and domains, like LeNet, AlexNet, VGGNet, ResNet, U-Net and Transformer.


Why is Convolutional Neural Network better than machine learning?

Convolutional Neural Network (CNN) is type of machine learning model that can learn from data and perform tasks like images recognition, natural language processing and computer vision. CNN is better than traditional machine learning model because it can capture spatial features and patterns in data Like edges, shapes and textures. 

What is the main advantage of Convolutional Neural Network?

One of main advantages of CNNs can extract features from images automatically, without need for manual feature engineering. They do this by applying filters to images which reduce amount of information and capture relevant patterns. CNNs can achieve high accuracy rates and are robust to noise and distortion in images.

What are the main components of Convolutional Neural Network?

CNN consists of three main components: input layer, output layer and one or more hidden layers. Hidden layers include one or more convolutional layers, which apply filters input data and produce feature maps. Convolutional layers are often followed by pooling layers, which reduce size and complexity of feature maps. Final hidden layer is usually fully connected layer, which connects all neurons from previous layer to the output layer. Output layer produces final prediction or classification of the input data.

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