Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network designed for processing structured grid-like data, such as images and videos. Introduced by Yann LeCun in 1989 for handwritten digit recognition, CNNs revolutionized computer vision by enabling systems to automatically learn hierarchical features from raw pixel data. A CNN typically consists of convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply filters to detect spatial patterns like edges and textures, pooling layers reduce spatial dimensions, and fully connected layers output predictions for classification or regression tasks.
https://en.wikipedia.org/wiki/Convolutional_neural_network
A key advantage of CNNs is their ability to perform feature extraction directly from raw input data, eliminating the need for manual feature engineering. Modern architectures like AlexNet, introduced in 2012, and ResNet, introduced in 2015, advanced the field by achieving state-of-the-art results in tasks such as image recognition and object detection. Transfer learning has further enhanced CNN applications, allowing pre-trained models to be fine-tuned for specific tasks, reducing computational requirements and training time. Frameworks like TensorFlow and PyTorch provide powerful tools for designing and implementing CNNs.
https://en.wikipedia.org/wiki/AlexNet
CNNs are widely used in real-world applications, from autonomous vehicle systems that detect pedestrians and traffic signs to medical imaging systems that identify tumors in X-rays and MRIs. In retail, they support visual search tools, enabling customers to find products using photos. Innovations like YOLO (You Only Look Once) for real-time object detection and GANs (Generative Adversarial Networks) for image synthesis further expand the scope of CNNs. As computational resources like GPUs and TPUs continue to improve, CNNs are expected to remain a cornerstone of deep learning research and applications.