The first and last layers are convolutional (C).

Wed, 06 May 2026 20:06:13 +0000

# The First and Last Layers Are Convolutional in CNNs: Understanding Their Role in Deep Learning In the world of deep learning, Convolutional Neural Networks (CNNs) have become the gold standard for image recognition, computer vision, and a wide range of visual tasks. A key architectural feature that distinguishes CNNs is the use of convolutional layers as both the first and the last layers in their structure. This unique design plays a crucial role in extracting meaningful features and preserving spatial information throughout the network. In this article, we explore why CNNs use convolutional layers at both the starting and ending points, their functional benefits, and how they collectively enable powerful visual understanding. ## Why Convolution Is the Foundation of the First Layer The journey of a CNN begins with its first layer, typically a convolutional layer. This is no accident — convolutional layers are uniquely suited for processing grid-like data such as images, where spatial relationships matter.