Explain the concept of convolutional neural networks (CNNs) and their significance in computer vision tasks, such as object recognition and image segmentation.

Convolutional Neural Networks (CNNs) are a specialized type of deep learning model designed to process and analyze visual data, such as images and videos. CNNs have revolutionized the field of computer vision and have become the cornerstone of many state-of-the-art solutions for tasks like object recognition, image classification, and image segmentation. The key concept behind CNNs is the utilization of convolutional layers. These layers apply convolution operations to input images, extracting local patterns and features. The convolution operation involves sliding a small matrix, called a kernel or filter, across the input image and computing dot products between the kernel and the corresponding input pixels. The output of this operation, known as a feature map, highlights specific spatial patterns or visual features present in the input image. CNNs typically consist of multiple layers stacked together, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers act as feature extractors, capturing low-level to high-level visual features hierarchically. The pooling layers downsample the feature maps, reducing the spatial dimensions while preserving important features. Finally, the fully connected layers incorporate these fea....