Convolutional Neural Network
Learn how convolutional neural networks process images through convolution, pooling, and classification layers. Understand key concepts like feature extraction, ReLU activation, and training with backpropagation to perform image classification.
What is an image?
Pixels are the building blocks of an image. A pixel is the intensity of light that appears in a given place in an image. An image with a resolution of 400 × 500 means it is 400 pixels wide and 500 pixels tall. Pixels in an image are presented in two ways:
- Grayscale (single channel)
- Color (three channel)
Grayscale images have pixel values between 0 and 255 and have one channel. Color images have 3 channels (Red, Green, and Blue). A single pixel in a color image is represented by three values, each depicting the amount of red, green, and blue. Each channel in color images also has defined values in the range of [0, 255], where 0 indicates no representation, and 1 indicates full representation.
Convolutional neural network
Convolutional neural networks are widely used for image-related tasks in the field of deep learning. They have proven effective in solving problems such as image classification, object detection, face recognition, and many more. In this lesson, we will explore the different components of a convolutional neural network and how an image passes through it.