Convolutional Neural Network is a subset of deep learning algorithms that uses neural networks to extract hierarchical features from input making them fit for tasks like image classification, natural language processing, and object detection.
In this blog, we will learn more about the Convolutional Neural Network, its features, workings, and applications.
What Is a Convolutional Neural Network?
Convolutional Neural Network is a subset of machine learning which is used in deep learning algorithms. They consist of an input layer, one or more hidden layers, an output layer, and node layers. Each node connects to each other and forms one another in a network. Above a specified threshold value the node gets activated and sends data to the next layer and so on.
Neural networks are used to perform various functions in datasets containing images, audio, or text. Different types of neural networks are used for different objectives. A Convolutional Neural Network is a subset of deep learning algorithms and a type of neural network used in the field of artificial intelligence to enable computers to understand and interpret images.
Different Layers In Convolutional Neural Network
There are three important layers in the convolutional neural network that are used to perform the complete functionality.
Input Layer
This layer is used to accept the raw data in the form of structured arrays. For images, the input is in the form of a multi-dimensional array representing the image in terms of pixel values.
This layer provides raw pixel data to For example, a colored image of size 64×64 had a shape of 64x64x3 where 3 represents the RGB Channels.
Hidden Layers
The hidden layers in Convolutional neural networks are used to handle special features in neural networking. They consist of layers such as a pooling layer, fully connected layers, and more. This layer is used to extract local features from input data and apply a set of filters for the input.
- Pooling Layer: It Reduces the spatial dimensions of the feature maps (downsampling) to decrease computation and focus on important features. It extracts and summarizes there are two types of this layer i,e. Max pooling and average pooling.
- Activation Layer: Introduces non-linearity into the network, enabling it to learn complex patterns. Common functions: ReLU, Sigmoid, and Tanh.
- Fully Connected Layer (FC): Connects all neurons from the previous layer to the next, serving as the decision-making layer.
Output Layer
This layer is used to convert the processed data from hidden layers and pass them into the final output. The number of neurons in the output layer corresponds to the number of possible classes. This layer uses extracted features for high level decision making and producing accurate and reliable products.
Working of Convolutional Neural Networks?
The Convolutional Neural Networks work using three interconnected layers. The first layer is the input layer which accepts raw input data i,e. The image is represented in a pixel value. Suppose you have an image that is represented as a cuboid of a definite length, width, and height. Height are the channels of the image and Width represents the dimension of the image.
The next working stage in a CNN is feature extraction like edges and textures. The pooling layer reduces the dimensions (2D into 1D Array) and retains significant features.
The final working stage of a Convolutional Neural Network consists of assigning class probabilities using a sigmoid function or a softmax.
Mathematical Overview of Convolutional Neural Network
The CNN revolves around learnable filters or kernels to input data. Each filter is designed to detect specific features such as edges or patterns in the input.
Convolutional layers contain a set of filters, typically smaller in size than the input dimensions. For instance, if the input data is an image with dimensions 32×32×3 (where 3 is RGB Channels) the filter dimensions can be a×a×3. Here
- “a” defines the filter’s height and width.
- The depth of the filter matches the depth of the input (3 in the case of RGB images).
The forward pass consists of certain operations such as sliding the filter across the input also known as stride. The filter is applied across the input volume by sliding it step by step in horizontal and vertical directions. Stride values are 1, 2 or higher for larger images.
At each step, the filter and patch of the input volume are multiplied and summed up to produce a single value. The feature map generates a 2D feature map for each filter. The output of the CNN layer is formed by stacking all the feature maps produced by the filters.
Also, check What is Artificial Neural Network (ANN)?
If the Number of filters is N, the output volume will have a depth of N, and its spatial dimension i,e. Height and width are determined by the following formula.
Where,
Input dimension: height/width of input data
Filter Size: the size of the filter
Padding: Extra borders are added around the input to control the output size.
Stride: Step size for sliding the filter
Benefits of Using Convolutional Neural Network
Some of the major benefits of using Convolutional Neural Networks are given below.
- Feature Learning: CNN Automatically extracts features without requiring manual feature engineering.
- High Accuracy: CNN Models excels in complex tasks like image and video recognition.
- Efficiency: It reduces parameters compared to traditional fully connected networks, improving training efficiency.
- Adaptability: Works well with both structured and unstructured data.
- Enhanced Visual Perception: Key component in advancing AI systems for vision-related tasks.
- Automation: It eliminates the need for manual feature extraction, accelerating AI development.
- Integration with Edge Devices: Enables AI in smartphones, IoT devices, and embedded systems for real-time applications.
- Cross-Domain Usability: CNNs are useful in diverse fields like healthcare, retail, and autonomous systems.
- Scalability: It helps in the design of deep, scalable networks for complex AI tasks.
Also, check, What is Machine Learning Models and Algorithms?
Applications of Convolutional Neural Network
Some of the major applications of CNNs are mentioned below.
- Image processing: It is used for image recognition and medical imaging.
- Natural Language Processing: Convolutional neural networks are used in NLP tasks like text classification, sentiment analysis, and more.
- Video Analysis: It is used in video analytics tasks like motion detection and action recognition.
- Security and Surveillance: It is also used in implementing security features using deep learning such as facial recognition,and anomaly detection.
- Autonomous systems: It is used in autonomous algorithms such as self-driving cars, robotic vision, etc.
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Convolutional Neural Network FAQ
Q1. What is a Convolutional Neural Network?
Ans: A CNN is a type of deep learning algorithm specifically designed to process grid-like data, such as images or time-series data. It uses convolutional layers to automatically extract hierarchical features from the input, making it highly effective for tasks like image classification, object detection, and natural language processing.
Q2. What are the main layers in CNN?
Ans: The main layers in CNNs are input layers, hidden layers (pooling layer, fully connected layer, activation layer), and output layer.
Q3. What is Stride?
Ans: Stride is a step size by which the filter moves across the input. A larger value of stride reduces the spatial dimensions of the output. While a smaller stride value preserves more spatial detail but increases computational cost.
Q4. What is Pooling in CNN?
Ans: Pooling in CNN is used to reduce the spatial dimension of feature maps from 2D to 1D. It helps focus the dominant features by summarizing regions of the feature map.
