b) Convolutional Neural Network (CNNs)

Convolutional Neural Network (CNN)

Convolutional Neural Networks (CNN’s) are particularly effective for image recognition and Computer Vision tasks due to their ability to detect patterns in images.

A Convolutional Neural Network (CNN), is a class of Artificial Neural Networks that specializes in processing data that has a grid-like topology, such as an image. A digital image is a binary representation of visual data. It contains a series of pixels arranged in a grid-like fashion that contains pixel values to denote how bright and what color each pixel should be.

The term ‘Convolutional’ means something that is complex and difficult to follow. In mathematics, ‘convolution’ is a mathematical operation on two functions that produces a third function that expresses how the shape of one is modified by the other. Here convolution refers to both the result function and to the process of computing it. The term "convolutional" in the context of "Convolutional Neural Networks" (CNNs) refers to the mathematical operation called "convolution" that is performed on the input data. Convolution is a key operation in CNNs, which enables them to efficiently process grid-like structured data, such as images, videos, and time series.

In a CNN, a convolutional layer applies multiple learnable filters (or kernels) to the input data, such as an image. Each filter slides across the input, performing an element-wise multiplication between the filter's weights and the corresponding input values (pixels in an image), followed by a summation of these products. This process generates a new grid-like structure called a feature map or activation map, which represents specific features or patterns detected by the filter.

Representation of image as a grid of pixels

Convolutional Neural Networks have been highly successful in various computer vision tasks, such as image classification, object detection, and segmentation, as well as other applications where grid-like structured data is present. The term "convolutional" highlights the central role of the convolution operation in these networks.

Qs? Why is it called a Convolutional Neural Network (CNN)?

Ans: The name "Convolutional Neural Network" (CNN) comes from the key operation used in this type of neural network architecture: the convolution. A convolution is a mathematical operation that combines two functions to produce a third function, which represents how one function modifies or affects the other. In the context of CNNs, the convolution operation is applied to the input data (such as images) using filters (or kernels) that slide over the input to detect specific features or patterns.

CNNs are designed to process grid-like data, such as images, by taking advantage of the spatial structure and local patterns present in the data. The convolutional layers in a CNN apply a set of learnable filters to the input data, effectively scanning the input for local patterns or features. Each filter is responsible for detecting a specific feature, such as edges, textures, or shapes. Convolutional layers help preserve the spatial structure of the input data and reduce the number of parameters compared to fully connected layers.

The term "convolutional" highlights the importance of the convolution operation in this type of neural network architecture. The convolution operation enables CNNs to efficiently capture local spatial patterns in the input data and learn hierarchical representations of the data, which is particularly useful for tasks like image classification, object detection, and segmentation.

In summary, the name "Convolutional Neural Network" (CNN) comes from the key operation used in this type of neural network architecture, the convolution. The convolution operation allows CNNs to efficiently process grid-like data, such as images, and learn hierarchical representations of the data by capturing local spatial patterns and features.

Qs? What is 'Convolutional' and 'Convolutional layers' in layman's terms?

Ans: In layman's terms, "convolutional" refers to a process that involves combining or blending two things together in a specific manner. In the context of a Convolutional Neural Network (CNN), the convolution operation is used to detect patterns or features in images by combining the image data with a set of filters or kernels.

"Convolutional layers" are the layers within a CNN that perform this convolution operation. They are designed to identify and extract local features or patterns in an input image, such as edges, textures, or shapes. To do this, the layer applies a set of learnable filters (small matrices) to the input image, which slide over the image in a grid-like pattern. Each filter is responsible for detecting a specific feature.

Imagine placing a small transparent window on top of an image and sliding it around the entire image. As you move the window, you observe what is inside the window and look for specific patterns (like a vertical edge or a circular shape). The convolutional layer works similarly, using filters to "look" for specific patterns in the input image.

In summary, "convolutional" refers to the process of combining image data with filters to detect patterns or features, while "convolutional layers" are the layers in a CNN that perform this operation. In layman's terms, convolutional layers act like small transparent windows that slide over an image, looking for specific patterns or features to help the neural network understand and process the image.