CNN - Deep Learning Neural Networks

Convolutional Neural Networks (CNN) represent a class of artificial neural networks specifically designed for tasks such as image recognition and speech processing. Inspired by the biological visual cortex, CNNs employ hierarchical structures and weight sharing mechanisms that significantly reduce the number of trainable parameters. The architecture typically consists of convolutional layers that extract spatial features using learnable filters, pooling layers for dimensionality reduction, and fully connected layers for final classification. CNNs initially demonstrated remarkable success in handwritten digit recognition and have since evolved into fundamental components of deep learning systems. Modern implementations leverage frameworks like TensorFlow or PyTorch, where key functions include Conv2D for convolutional operations, MaxPooling for feature downsampling, and activation functions like ReLU for introducing non-linearity. Today, CNNs serve as cornerstone technologies in computer vision applications (object detection, segmentation), natural language processing (text classification), and audio signal processing, with advanced variants incorporating techniques like batch normalization and dropout for improved training stability and generalization performance.