Convolutional Neural Network
Convolutional Neural Networks are designed specifically to deal with image data. These are made up of a series of layers that learn to extract relevant features out of an image. A Convolutional Neural Network(CNN) takes in an image and applies a series of different image filters, known as convolutional kernels to extract the features like edges of objects in that image or the colors that distinguish the different classes of images.
In general, a deep neural network takes an array of vectors and finds the hidden relations between the inputs and the target variable. But how does a deep neural network deal with image data?
An image is formed by a grid of pixels. Each pixel is associated with a value that represents the color or intensity of light that appears in a given place in an image. A grayscale image contains pixel values from 0 to 255, whereas a colored image(RGB) contains a vector of pixel values ranging from 0 to 255.
Let’s take a look at the following image to see how an image(grayscale) is represented in the form of pixels.